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This paper presents an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for video streaming applications using best effort protocols. LCEVC is a new video standard by MPEG, which enhances any base codec through an additional low bitrate stream, improving both video compression efficiency and and transmission. However, there is an interplay between packetization, packet loss visibility, choice of codec and video quality, which implies that prior studies with other codecs may be not as relevant. The contributions of this paper is, therefore in twofold: It evaluates the compression performance of LCEVC and then the impact of packet loss on its video quality when compared to H.264 and HEVC.The results from this evaluation suggest that, regarding compression, LCEVC outperformed its base codecs, overall in terms average encoding bitrate savings when using the constant rate factor (CRF) rate control. For example at a CRF of 19, the average encoding bitrate was reduced by 18.7% and 15.8% when compared with the base H.264 and HEVC codecs respectively. Furthermore, LCEVC produced better visual quality across the packet loss range compared to its base codecs and the quality only started to decrease once packet loss exceeded 0.8-1%, and decreases at a slower pace compared to its equivalent base codecs. This suggests that the LCEVC enhancement layer also provides error concealment. The results presented in this paper will be of interest to those considering the LCEVC standard and expected video quality in error-prone environments [ABSTRACT FROM AUTHOR]

The growing need for high-definition video material requires improvements in video encoding systems that maximize encoding performance while simultaneously improving compression efficiency. This paper presents a novel genetic algorithm-based intra-coding optimization method for the H.266/Versatile Video Coding (VVC) standard. One of the biggest problems in video compression is finding the ideal balance between encoding speed and video quality, which is what our approach aims to solve. Our suggested method makes use of the strong search capabilities of the evolutionary algorithm to choose the best Multi-Type Tree (MTT) partitions and coding tools from the wide range of possibilities present in H.266/VVC. The wellness assessment work that guides this choice method combines criteria for perceptual appraisal of video quality and measures for coding productivity appraisal. [ABSTRACT FROM AUTHOR]

ColorVideoVDP is a video and image quality metric that models spatial and temporal aspects of vision for both luminance and color. The metric is built on novel psychophysical models of chromatic spatiotemporal contrast sensitivity and cross-channel contrast masking. It accounts for the viewing conditions, geometric, and photometric characteristics of the display. It was trained to predict common video-streaming distortions (e.g., video compression, rescaling, and transmission errors) and also 8 new distortion types related to AR/VR displays (e.g., light source and waveguide non-uniformities). To address the latter application, we collected our novel XR-Display-Artifact-Video quality dataset (XR-DAVID), comprised of 336 distorted videos. Extensive testing on XR-DAVID, as well as several datasets from the literature, indicate a significant gain in prediction performance compared to existing metrics. ColorVideoVDP opens the doors to many novel applications that require the joint automated spatiotemporal assessment of luminance and color distortions, including video streaming, display specification, and design, visual comparison of results, and perceptually-guided quality optimization. The code for the metric can be found at https://github.com/gfxdisp/ColorVideoVDP. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are widely used nowadays, as numerous applications in various fields can take advantage of UAVs. Wireless communication has received a lot of attention in recent years, as it is one of the most critical enabling technologies for UAVs. Direct link technology, commonly used for UAV communication, can constrain the UAV's operational range and hinder applications in environments with low probability of line-of-sight (LoS). Due to these limitations, simple direct link communication cannot be a scalable solution for future large-scale UAV deployment. Cellular networks are the answer for future development of UAV communication technology, especially with the forthcoming 5G cellular network. It is thought that cellular-enabled UAV communication will provide high-rate and delay-sensitive communication for UAV applications in real-time scenarios. However, despite the promising advantages of cellular-enabled UAVs, the network coverage for UAVs differs slightly from the conventional 2D range for terrestrial users. UAVs operate at high altitudes and speeds, necessitating cellular base stations to provide 3D aerial coverage for UAV users instead of 2D range for terrestrial users. In this paper, the performance of cellular-connected UAVs is investigated by identifying the effects of altitude on the performance of cellular-connected UAVs and video transmission efficiency of cellular-connected UAVs at various video resolutions. This study evaluates UAV performance by obtaining the rate of received signal (Rx), rate of transmit signal (Tx) and latency based on received signal strength indicator (RSSI) values at various altitudes obtained from a previous study. The UAV Cast-Pro software was used to collect data consisting of Rx, Tx and latency values at different video resolutions of 640 x 480, 1,280 x 720, 1,640 x 922 and 1,920 x 1,080 p. The results show that cellular-connected UAVs with reliable cellular network can produce good quality video streaming for various video resolutions at operational altitude of 50 m. [ABSTRACT FROM AUTHOR]

The International Football Association Board decided to introduce Video Assistant Referee (VAR) in 2018. This led to the need to develop methods for quality control of the VAR-systems. This article focuses on the important aspect to evaluate the video quality. Video Quality assessment has matured in the sense that there are standardized, commercial products and established open-source solutions to measure it with objective methods. Previous research has primarily focused on the end-user quality assessment. How to assess the video in the contribution phase of the chain is less studied. The novelties of this study are two-fold: 1) The user study is specifically targeting video experts i.e., to assess the perceived quality of video professionals working with video production. 2) Six video quality models have been independently benchmarked against the user data and evaluated to show which of the models could provide the best predictions of perceived quality. The independent evaluation is important to get unbiased results as shown by the Video Quality Experts Group. An experiment was performed involving 25 video experts in which they rated the perceived quality. The video formats tested were High-Definition TV both progressive and interlaced as well as a quarters size format that was scaled down half the size in both width and height. The videos were encoded with both H.264 and Motion JPEG for the full size but only H.264 for the quarter size. Bitrates ranged from 80 Mbit/s down to 10 Mbit/s. We could see that for H.264 that the quality was overall very good but dropped somewhat for 10 Mbit/s. For Motion JPEG the quality dropped over the whole range. For the interlaced format the degradation that was based on a simple deinterlacing method did receive overall low ratings. For the quarter size three different scaling algorithms were evaluated. Lanczos performed the best and Bilinear the worst. The performance of six different video quality models were evaluated for 1080p and 1080i. The Video Quality Metric for Variable Frame Delay had the best performance for both formats, followed by Video Multimethod Assessment Fusion method and the Video Quality Metric General model. [ABSTRACT FROM AUTHOR]

Objective: Clinical record-keeping is recognized as a core competency in medical education. This study aimed to evaluate comprehensiveness and instructional quality of videos available on the YouTube platform for teaching clinical record-keeping. Methods: YouTube was searched by using relevant keywords. Based on eligibility criteria, 59 videos were included in the study. Videos were assessed for country of origin, video length, number of likes, dislikes, comments, daily views, like ratio, and video power index. Tools to measure the quality of clinical notes, which are QNOTE and RED Checklist, have been used to assess comprehensiveness of the videos. Instructional quality was assessed using the instructional video quality checklist (IVQC). Results: The comprehensiveness score was 60.4±17.89 (out of 100), while instructional quality score was 11.19±3.61 (out of 27). IVQC scores were significantly higher in the university/professional organizations and academics compared to the others (p<0.001). However, there was no significant difference between the groups in comprehensiveness scores (p=0.131). Conclusions: YouTube videos missing important rate of components of clinical record-keeping. Moreover, the instructional quality of the videos falls below the expected levels. These problems still persist in the videos uploaded by universities/professional organizations and academics. Therefore, YouTube videos should be used cautiously for clinical record-keeping training by medical students and medical educators. [ABSTRACT FROM AUTHOR]

This paper presents a genetic approach for optimizing intra coding in H.266/VVC. The proposed algorithm efficiently selects coding tools and Multi-Type Tree (MTT) partitions to achieve a balance between encoding time and video quality. The fitness evaluation function, which combines perceptual metrics and coding efficiency metrics, is used to assess the quality of each candidate solution. The results demonstrate a significant reduction in encoding time without compromising video quality. The proposed algorithm selects coding tools from a set of available tools in H.266/VVC. These tools include intra prediction modes, transform units, quantization parameters, and entropy coding modes. The MTT partitioning scheme includes four types of partitions: quadtree, binary tree, ternary tree, and quad-binary tree. Perceptual metrics are used to evaluate the visual quality of the encoded video. Coding efficiency metrics are used to evaluate the coding efficiency of the encoded video. The fitness evaluation function combines perceptual metrics and coding efficiency metrics to assess the quality of each candidate solution.

Objective: This study aimed to evaluate the quality, reliability, and content usefulness of videos created by orthodontists on clear orthodontic aligners. Methods: Videos were screened using YouTubeTM by conducting a search for “Invisalign”. After a preliminary evaluation of the first 250 results, 61 videos that met the selection criteria were scored and their length, days since upload, and numbers of views, likes, dislikes, and comments were recorded. These data were used to calculate the interaction index and viewing rate. Video reliability was assessed using a five-item modified DISCERN index, and video quality was assessed using the Video Information and Quality Index. A 10-item content usefulness index was created to determine the usefulness of the video content. Descriptive statistics of the parameters were calculated, and correlation coefficients were calculated to evaluate the relationships between the parameters. Results: The mean reliability score was 2.75±1.02 (out of 5), and the total quality score was 11.80±3.38 (out of 20). The total content usefulness index was quite low, with a mean score of 2.52±2.14 (out of 10). Interaction index and viewing rate were positively correlated with reliability score (r=0.463, p

Nowadays, people watch network video through any way, such as mobile phone, tables. However, during the transmission process of network video, the video quality may be impaired by many factors. So how to assess the video quality has become a hot research topic in the academic community. This paper proposes objective assessment method based on fuzzy neural network. At first, the impairment factors of video quality are introduced, next the experimental environment is built, and two fuzzy neural network models are used to build objective assessment method. By adjusting the model structure and training times, the objective scores of video quality are calculated. At the same time, other recent objective methods are compared with the proposed method. Lastly the advantages of two models are analyzed, and the detail process of them will be discussed. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the quality, reliability, and content usefulness of videos created by orthodontists on clear orthodontic aligners. Methods: Videos were screened using YouTubeTM by conducting a search for "Invisalign". After a preliminary evaluation of the first 250 results, 61 videos that met the selection criteria were scored and their length, days since upload, and numbers of views, likes, dislikes, and comments were recorded. These data were used to calculate the interaction index and viewing rate. Video reliability was assessed using a five-item modified DISCERN index, and video quality was assessed using the Video Information and Quality Index. A 10-item content usefulness index was created to determine the usefulness of the video content. Descriptive statistics of the parameters were calculated, and correlation coefficients were calculated to evaluate the relationships between the parameters. Results: The mean reliability score was 2.75±1.02 (out of 5), and the total quality score was 11.80±3.38 (out of 20). The total content usefulness index was quite low, with a mean score of 2.52±2.14 (out of 10). Interaction index and viewing rate were positively correlated with reliability score (r=0.463, p<0.01; r=0.295, p<0.05) and total quality score (r=0.365, p<0.01; r=0.295, p<0.01, respectively). The reliability score was positively correlated with the total quality score (r=0.842, p<0.01) and total content usefulness index (r=0.346, p<0.01). Conclusion: Videos about orthodontic aligner treatment have average reliability and quality but largely insufficient content. [ABSTRACT FROM AUTHOR]

One of the elements influencing users’ perception of video quality is the viewing distance. Although TVs have grown in size and resolution, viewers’ watching behavior and living room conditions have not changed equally and therefore viewers sit at distances farther than optimal. This gives rise to the question whether higher resolutions are perceptually beneficial in such scenarios. Therefore, this paper presents four studies that investigate the improvements offered by 8K/UHD-2 in comparison to 4K/UHD-1 using HDR videos with viewing distances between 0.50H and 3.00H with H representing the screen height. Study 1 identifies general effects while Study 2 refines the methodology and aims at finding the distance of subjective quality parity. Study 3 includes encoded contents to better represent a real-world streaming scenario. Study 4 looks into the effects of watching contents once or repeatedly per test and includes a wider variety of self created sources. The findings of the studies provide quantitative data on the effect of viewing distance, showing how the slight video quality advantage of 8K HDR against 4K HDR content at the design viewing distance of 0.80H diminishes as the viewing distance increases, with no or very little improvements beyond 2.60H, depending on the content. In general, the degree of enhancement is related to the spatial and temporal content complexity, as well as the bitrate settings employed for encoding. Additionally, it is found that, on average, subjects prefer to sit at viewing distances of around 2.20H. For most people this may be too far away to truly appreciate the improvements offered by the 8K resolution.

In the field of surgical tele-education, the need for high-quality real-time video transmission in real-time networks is crucial and often compromised by delays and inefficiency. This paper provides a comprehensive study of enhancing the real-time transmission of video in the field of surgical tele-education. The primary focus is on resolving the significant issues of delays in transmission and deterioration in video quality. We propose an improved approach called the Enhanced Real-time Video (ERV) algorithm that greatly improves the efficiency of bandwidth usage, reduces the latency from start to finish, and guarantees the dependability of video transmission. The ERV algorithm is intricately crafted to assess and choose the most dependable communication route, therefore optimizing the transmission duration for video packets that are vital for real-time applications. Comparative analyses with existing approaches demonstrate that the ERV algorithm significantly improves the Peak Signal-to-Noise Ratio (PSNR) of videos, obtaining an average of 58.53 db. This represents a noteworthy enhancement compared to the prior benchmark of 51.878 db. In addition, the method exhibits a faster processing time, with an average of 40.4 milliseconds, which is much better than the existing standard of 64.3 milliseconds. This enhancement is crucial in situations when even the smallest unit of time is significant, such as live broadcasts of surgical procedures. The ERV method significantly improves transmission efficiency by reducing the average end-to-end delay in real-time video streaming from 132.77 ms to 82.3 ms. The primary achievement of this research is its thorough methodology to reduce latency and enhance transmission dependability, therefore improving the quality of real-time video in surgical tele-education.

The increasing popularity of video streaming services and the widespread accessibility of high-speed internet underscore the importance of delivering cost-effective and seamless streaming experiences. Shared internet connections may lead to varying speeds, impacting Quality of Experience (QoE). Rate adaptation techniques aim to ensure smooth video transmission, but overly optimistic adaptations can compromise user experience. Objective video quality assessment is crucial for efficient rate adaptation to ensure smooth QoE. This research proposes a novel method incorporating temporal channel shifting into Convolutional Neural Networks (CNN) for video quality assessment while maintaining the computational simplicity of a 2D CNN model. The proposed approach relies on the EfficientNet architecture, initially pre-trained on quality-aware images, and fine-tune it using datasets of rate-adaptive videos. The model is trained and evaluated on two benchmark datasets, namely “Waterloo sQoE III” and “LIVE Netflix II,” which consist of rate-adaptive videos annotated with subjective quality scores. Experimental results encompass the evaluation of Pearson, Spearman, and Kendall correlation coefficients, along with the computation time ratio for the proposed approach. The outcomes reveal competitive scores of 0.795, 0.652, 0.772, and 0.216 for the “Live Netflix II dataset” and 0.782, 0.713, 0.721, and 0.230 for the “Waterloo sQoE III dataset.” Our proposed method, compared to 24 approaches for “Waterloo sQoE III” and 25 for “LIVE Netflix II,” attains the highest correlation scores while maintaining near-real-time processing efficiency. These results affirm the efficacy of our approach in accurately predicting human judgment (QoE) with computational efficiency.

We propose a deep learning-based cross-layer power allocation method for asymmetric cell-free massive MIMO video communication systems. The proposed cross-layer approach considers physical layer channel state information (CSI) and the application layer rate distortion (RD) function, and it aims to enhance video quality in terms of peak signal-to-noise ratio (PSNR). Our study develops a decentralized deep neural network (DNN) model to capture intricate system patterns, enabling accurate and efficient power allocation decisions. The proposed cross-layer approach includes unsupervised and hybrid (supervised/unsupervised) learning models. The numerical results show that the hybrid method achieves convergence with just 50% of the iterations required by the unsupervised learning model and that it achieves a 1 dB gain in PSNR over the baseline physical layer scheme. [ABSTRACT FROM AUTHOR]

In the quest to optimize user experience, network, and service, providers continually seek to deliver high-quality content tailored to individual preferences. However, predicting user perception of quality remains a challenging task, given the subjective nature of human perception and the plethora of technical attributes that contribute to the overall viewing experience. Thus, we introduce a Fuzzy Logic-bAsed ModEl for Video Quality Assessment (FLAME-VQA), leveraging the LIVE-YT-HFR database containing 480 video sequences and subjective ratings of their quality from 85 test subjects. The proposed model addresses the challenges of assessing user perception by capturing the intricacies of individual preferences and video attributes using fuzzy logic. It operates with four input parameters: video frame rate, compression rate, and spatio-temporal information. The Spearman Rank–Order Correlation Coefficient (SROCC) and Pearson Correlation Coefficient (PCC) show a high correlation between the output and the ground truth. For the training, test, and complete dataset, SROCC equals 0.8977, 0.8455, and 0.8961, respectively, while PCC equals 0.9096, 0.8632, and 0.9086, respectively. The model outperforms comparative models tested on the same dataset. [ABSTRACT FROM AUTHOR]

Summary of backgroundDementia is among the leading causes of death and disability worldwide, having a major impact not only on the affected person but also on all of society. The Internet is a popular and growing source of health-related information for patients, family members, carriers, and physicians. TikTok, one of the most popular social media platforms, is an important source for knowledge access and adoption. However, the quality of health information on TikTok has not been sufficiently studied.ObjectiveTo evaluate the quality of the information provided in the most popular videos on dementia shared on TikTok.Study designA cross-sectional study.MethodsThe top 100 most popular videos on TikTok obtained by searching the hashtag “dementia” were included in the study and grouped based on their source and content. The popularity of the videos was estimated based on the numbers of likes, comments, and shares. The quality of health-related information was evaluated using the DISCERN score and the Global Quality Score (GQS).ResultsVideos had a median duration of 33.29 s; the median number of likes was 635,100, with a total of 93,698,200 likes, 903,859 comments, and 5,310,912 shares. The source (uploader) of 65% of the videos was family members, while only 4% were uploaded by doctors. The content was lifestyle-related in 62% of the videos, while 12% of the videos were for fun. Videos had a median DISCERN score of 22.5 (IQR 20–27) and a median GQS of 2 (IQR 1–3). The videos uploaded by doctors had the highest quality scores and the lowest popularity.ConclusionThe most popular dementia videos on TikTok are mostly shared by family members and are of poor quality. Given the major public health issues associated with dementia, experts must provide appropriate and active assistance to patients in interpreting the information identified.

• Youtube is an important source of information in the field of health in recent years. • Spasticity is an important clinical phenomenon that can develop particularly after several neurological disorders. • Most of the youtube videos on spasticity are of good quality and reliable. • Physicians should caution their patients to use YouTube to watch videos uploaded by health professionals. Youtube has become an influential source of health. This study aimed to evaluate the reliability and quality of YouTube videos on spasticity. The keywords " spasticity, spasticity treatment, spasticity exercises" were used to search for videos. According to the search results, 180 videos were analyzed, videometric characteristics of the videos were recorded, and 2 groups were formed as health professionals and non-health professionals according to the video source. In addition, low, medium and high quality groups were formed using the global quality score (GQS). The reliability of the videos was evaluated using the modified DISCERN (mDISCERN) scale. Video popularity was assessed using the video power index (VPI). After excluding videos that met the exclusion criteria, the remaining 68 videos were analyzed. The videos were uploaded by healthcare professionals (n = 47, 69.1%) and non-healthcare professionals (n = 21, 30.9%). The popularity (VPI), reliability (mDISCERN) and quality (GQS) of videos uploaded by healthcare professionals were significantly higher (p = 0.002, p = 0.001, p = 0.021, respectively). Most of the videos were of high quality according to GQS (n = 40, 58.8%). All of the high quality videos were of healthcare professionals. The number of sources from healthcare professionals was significantly higher in high quality videos than in both low (p = 0.001) and medium (p = 0.001) quality videos. We can conclude that most of the YouTube videos on spasticity are reliable and of high quality. However, it should be kept in mind that patients may be exposed to low-quality and unreliable videos with misleading content. [ABSTRACT FROM AUTHOR]

The internet-based broadcasting has led to emergency of a great number of broadcasting channels that offer subscribers with more choices ubiquitously. Understanding of the determinants for customers' subscription to a particular broadcasting channel becomes important. This study analyzed factors influencing online Television (TV) Subscription in Tanzania. Specifically, the study assessed the influence of information quality, video quality and online TV technology ease of use on online TV subscription in Tanzania. A questionnaire was used to collect data from 241 online TV subscribers in Dar es Salaam. A multiple regression analysis of the data collected revealed that, information quality and online TV technology ease of use influence significantly online TV subscription in Tanzania. However, the findings of the study established that video quality influence Online TV subscription insignificantly. This implies that, the video quality did not matter most for online TV subscription. The study recommends that, Online TV service providers should focus on improving content quality of the information shared. The platforms for accessing Online TVs should be ease to use including making it easy to access and use online TV programs in a more cost effective manner. Besides, subscribers should be easily enabled to access and view TV programs and/or recorded videos faster than it is with traditional TVs. Doing that would attract more subscribers and hence generating more revenue through subscription-based and ad-supported models. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to assess video quality, spatial and social presence, empathy, attitude, and attention in 360-degree videos for immersive communications. The methodology is validated in an experiment which simulates an immersive communication environment where participants attend three conversations of different genre (everyday conversation, educational, and discussion) and from actor and observer acquisition perspectives. We consider three experimental conditions: (A) visualizing and rating the perceptual quality of contents in a Head-Mounted Display (HMD), (B) visualizing the contents in an HMD, and (C) visualizing the contents in an HMD where participants can see their hands and take notes. In all conditions participants visualize the same 360-degree videos, designed and acquired in the context of international experiences. Fifty-four participants were evenly distributed among A, B, and C conditions taking into account their international experience backgrounds (working or studying in a foreign country), obtaining a balanced and diverse sample of participants. In this paper, video quality is evaluated with Single-Stimulus Discrete Quality Evaluation (SSDQE) methodology. Spatial and social presence are evaluated with questionnaires adapted from the literature. Initial empathy is assessed with Interpersonal Reactivity Index (IRI) and a questionnaire is designed to evaluate the attitude after the visualization of each video. Attention is evaluated with three questions about the conversations of the contents that had pass/fail answers. The results from the subjective test validate the proposed methodology in immersive communications, showing that video quality experiments can be adapted to conditions imposed by experiments focused on the evaluation of socioemotional features in terms of contents of long-duration, different acquisition perspectives, and genre. In addition, the positive results related to the sense of social and spatial presence imply that technology can be relevant in the analyzed use case. Other main result is that the acquisition perspective greatly influences social presence. Finally, the annotated dataset, Student Experiences Around the World dataset (SEAW-dataset), obtained from the experiment is made publicly available for the research community. [ABSTRACT FROM AUTHOR]

A well‐known drawback with LCD‐displays in cold is a slow pixel response leading to poor picture quality. Low temperatures can constitute a hazard in viewing important displays in cars. Perceptual experiments with 20 test‐persons were conducted to find clear and acceptable ranges on screens simulating distortions in low temperatures. The results showed perception over clear and acceptable image quality was impaired beyond ‐20°C for the LCD‐screen in the experiments. [ABSTRACT FROM AUTHOR]

In this paper, an error-correction code (ECC)-enabled memory design with selective bit hardening is proposed for approximate data storage. Approximate memory is suitable for error-tolerant applications, and can achieve low-power design goals within targeted quality. Our design offers approximately 90% power savings compared to traditional memory, without compromising perceived quality. To the best of our knowledge, combining selective bit hardening with ECC is a novel approach to ensure the protection of MSBs and protect other faulty bits, respectively. Our proposed method requires no area overhead for MSB hardening, other than one additional input pin; and area overhead for implementing ECC is minimal.

Video data received for analysis often come in a variety of file formats and compression schemes. These data are often transcoded to a consistent file format for forensic examination and/or ingesting into a video analytic system. The file format often requested is an MP4 file format. The MP4 file format is a very common and a universally accepted file format. The practical application of this transcoding process, across the analytical community, has generated differences in video quality. This study sought to explore possible origins of the differences and assist the practitioner by defining minimum recommendations to ensure that quality of the video data is maintained through the transcoding process. This study sought to generate real world data by asking participants to transcode provided video files to an MP4 file format using programs they would typically utilize to perform this task. The transcoded results were evaluated based on measurable metrics of quality. As the results were analyzed, determining why these differences might have occurred became less about a particular software application and more about the settings employed by the practitioner or of the capabilities of the program. This study supports the need for any video examiner who is transcoding video data to be cognizant of the settings utilized by the programs employed for transcoding video data, as loss of video quality can affect analytics as well as further analysis. [ABSTRACT FROM AUTHOR]

Featured Application: The results of the research may be helpful in the setup of video quality assessment procedures in order to achieve results as close as possible to the quality experienced by the end users of the video streaming services. The paper presents the results of subjective and objective quality assessments of H.264-, H.265-, and VP9-encoded video. Most of the literature is devoted to subjective quality assessment in well-defined laboratory circumstances. However, the end users usually watch the films in their home environments, which may be different from the conditions recommended for laboratory measurements. This may cause significant differences in the quality assessment scores. Thus, the aim of the research is to show the impact of environmental conditions on the video quality perceived by the user. The subjective assessment was made in two different environments: in the laboratory and in users' homes, where people often watch movies on their laptops. The video signal was assessed by young viewers who were not experts in the field of quality assessment. The tests were performed taking into account different image resolutions and different bit rates. The research showed strong correlations between the obtained results and the coding bit rates used, and revealed a significant difference between the quality scores obtained in the laboratory and at home. As a conclusion, it must be underlined that the laboratory tests are necessary for comparative purposes, while the assessment of the video quality experienced by end users should be performed under circumstances that are as close as possible to the user's home environment. [ABSTRACT FROM AUTHOR]

Changes in the footballing world's approach to technology and innovation, along with major advancements in broadcasting contributed to the decision by the International Football Association Board to introduce Video Assistant Referees in 2018. The change meant that under strict protocols referees could use video replays to review decisions in the event of a "clear and obvious error" or a "serious missed incident". At the time of writing 48-Member Associations have introduced the Video Assistant Referees protocol in at least one of their tournaments and there are many technology providers who work with organisers to implement the Video Assistant Referees systems. To ensure that the use of Video Assistant Referees has a positive effect on the game, Fédération Internationale de Football Association collaborated with the RISE Research Institutes of Sweden to develop objective test methods that could be used to ensure that a system can provide an adequate solution. Each provider must be able to pass requirements that ensure that they can deal with the challenges of processing, coding, decoding, synchronising, and re-formatting of the broadcast feeds. This article will describe the development of the test methods and illustrate some initial results from a test event on Video Assistant Referees system candidates. The methods have shown to be robust and appropriate for their intended purpose and will be developed over the years to ensure the quality of Video Assistant Referees. The developed measurement methods are general and can be applied to other broadcast and video systems as well as to other sports. [ABSTRACT FROM AUTHOR]

Video delivered over IP networks in real-time applications, which utilize RTP protocol over unreliable UDP such as videotelephony or live-streaming, is often prone to degradation caused by multiple sources. The most significant is the combined effect of video compression and its transmission over the communication channel. This paper analyzes the adverse impact of packet loss on video quality encoded with various combinations of compression parameters and resolutions. For the purposes of the research, a dataset containing 11,200 full HD and ultra HD video sequences encoded to H.264 and H.265 formats at five bit rates was compiled with a simulated packet loss rate (PLR) ranging from 0 to 1%. Objective assessment was conducted by using peak signal to noise ratio (PSNR) and Structural Similarity Index (SSIM) metrics, whereas the well-known absolute category rating (ACR) was used for subjective evaluation. Analysis of the results confirmed the presumption that video quality decreases along with the rise of packet loss rate, regardless of compression parameters. The experiments further led to a finding that the quality of sequences affected by PLR declines with increasing bit rate. Additionally, the paper includes recommendations of compression parameters for use under various network conditions. [ABSTRACT FROM AUTHOR]

Video content on the Internet continues to grow. As a result, streaming platforms must ensure a certain level of quality when preparing their content. To this end, several metrics have been developed by the research community to evaluate video quality. This work integrates 14 video metrics and the SI-TI indicators into a container image to create a cross-platform tool, VQMTK. The tool offers a web interface and a Bash script that combines all metrics into a single tool. Performance tests have demonstrated that the tool is capable of handling all the integrated metrics using 4K video samples. The tool can be used in scientific and educational environments.

Social networks change the way and approaches of video spread and promote range and speed of video spread, which results in frequent traffic blowout and a heavy load on the networks. The social and geographical communication efficiency determines the efficiency of video sharing, which enables the eruptible traffic to be offloaded in underlaying networks to relieve the load of networks and ensure the user quality of the experience. In this paper, we propose a novel geo-social-aware video edge delivery strategy based on the modeling of the social-geographical dynamic in urban area (GSVD). By investigating the frequency of sharing behaviors, social communication efficiency, and efficiency of social sub-network consisting of one-hop social neighbors of users, GSVD estimates the interactive and basic social relationship to calculate the closeness of the social relationship between mobile users. GSVD makes use of grid partition and coding subarea to express the geographical location of mobile users and designs a calculation method of coding-based geographical distance. GSVD considers the dynamic update of social distance and geographical location and designs a measurement of video delivery quality in terms of delivery delay and playback continuity. A strategy of video delivery with the consideration of adapting to social-geographical dynamic is designed, which effectively promotes the efficiency of video sharing. Extensive tests show how GSVD achieves much better performance results in comparison with other state of the art solutions. [ABSTRACT FROM AUTHOR]

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client–server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video. [ABSTRACT FROM AUTHOR]

We propose a deep learning-based cross-layer power allocation method for asymmetric cell-free massive MIMO video communication systems. The proposed cross-layer approach considers physical layer channel state information (CSI) and the application layer rate distortion (RD) function, and it aims to enhance video quality in terms of peak signal-to-noise ratio (PSNR). Our study develops a decentralized deep neural network (DNN) model to capture intricate system patterns, enabling accurate and efficient power allocation decisions. The proposed cross-layer approach includes unsupervised and hybrid (supervised/unsupervised) learning models. The numerical results show that the hybrid method achieves convergence with just 50% of the iterations required by the unsupervised learning model and that it achieves a 1 dB gain in PSNR over the baseline physical layer scheme.

In the quest to optimize user experience, network, and service, providers continually seek to deliver high-quality content tailored to individual preferences. However, predicting user perception of quality remains a challenging task, given the subjective nature of human perception and the plethora of technical attributes that contribute to the overall viewing experience. Thus, we introduce a Fuzzy Logic-bAsed ModEl for Video Quality Assessment (FLAME-VQA), leveraging the LIVE-YT-HFR database containing 480 video sequences and subjective ratings of their quality from 85 test subjects. The proposed model addresses the challenges of assessing user perception by capturing the intricacies of individual preferences and video attributes using fuzzy logic. It operates with four input parameters: video frame rate, compression rate, and spatio-temporal information. The Spearman Rank–Order Correlation Coefficient (SROCC) and Pearson Correlation Coefficient (PCC) show a high correlation between the output and the ground truth. For the training, test, and complete dataset, SROCC equals 0.8977, 0.8455, and 0.8961, respectively, while PCC equals 0.9096, 0.8632, and 0.9086, respectively. The model outperforms comparative models tested on the same dataset.

The paper presents $AVQBits$ , a versatile, bitstream-based video quality model. It can be applied in several contexts such as video service monitoring, evaluation of video encoding quality, of gaming video QoE, and even of omnidirectional video quality. In the paper, it is shown that $AVQBits$ predictions closely match video quality ratings obained in various subjective tests with human viewers, for videos up to 4K-UHD resolution (Ultra-High Definition, 3840 x 2180 pixels) and framerates up 120 fps. With the different variants of $AVQBits$ presented in the paper, video quality can be monitored either at the client side, in the network or directly after encoding. The no-reference $AVQBits$ model was developed for different video services and types of input data, reflecting the increasing popularity of Video-on-Demand services and widespread use of HTTP-based adaptive streaming. At its core, $AVQBits$ encompasses the standardized ITU-T P.1204.3 model, with further model instances that can either have restricted or extended input information, depending on the application context. Four different instances of $AVQBits$ are presented, that is, a Mode 3 model with full access to the bitstream, a Mode 0 variant using only metadata such as codec type, framerate, resoution and bitrate as input, a Mode 1 model using Mode 0 information and frame-type and -size information, and a Hybrid Mode 0 model that is based on Mode 0 metadata and the decoded video pixel information. The models are trained on the authors’ own AVT-PNATS-UHD-1 dataset described in the paper. All models show a highly competitive performance by using AVT-VQDB-UHD-1 as validation dataset, e.g., with the Mode 0 variant yielding a value of 0.890 Pearson Correlation, the Mode 1 model of 0.901, the hybrid no-reference mode 0 model of 0.928 and the model with full bitstream access of 0.942. In addition, all four $AVQBits$ variants are evaluated when applying them out-of-the-box to different media formats such as 360° video, high framerate (HFR) content, or gaming videos. The analysis shows that the ITU-T P.1204.3 and Hybrid Mode 0 instances of $AVQBits$ for the considered use-cases either perform on par with or better than even state-of-the-art full reference, pixel-based models. Furthermore, it is shown that the proposed Mode 0 and Mode 1 variants outperform commonly used no-reference models for the different application scopes. Also, a long-term integration model based on the standardized ITU-T P.1203.3 is presented to estimate ratings of overall audiovisual streaming Quality of Experience (QoE) for sessions of 30 s up to 5 min duration. In the paper, the $AVQBits$ instances with their per-1-sec score output are evaluated as the video quality component of the proposed long-term integration model. All $AVQBits$ variants as well as the long-term integration module are made publicly available for the community for further research.

In the design of action recognition models, the quality of videos is an important issue; however, the trade-off between the quality and performance is often ignored. In general, action recognition models are trained on high-quality videos, hence it is not known how the model performance degrades when tested on low-quality videos, and how much the quality of training videos affects the performance. The issue of video quality is important, however, it has not been studied so far. The goal of this study is to show the trade-off between the performance and the quality of training and test videos by quantitative performance evaluation of several action recognition models for transcoded videos in different qualities. First, we show how the video quality affects the performance of pre-trained models. We transcode the original validation videos of Kinetics400 by changing quality control parameters of JPEG (compression strength) and H.264/AVC (CRF). Then we use the transcoded videos to validate the pre-trained models. Second, we show how the models perform when trained on transcoded videos. We transcode the original training videos of Kinetics400 by changing the quality parameters of JPEG and H.264/AVC. Then we train the models on the transcoded training videos and validate them with the original and transcoded validation videos. Experimental results with JPEG transcoding show that there is no severe performance degradation (up to −1.5%) for compression strength smaller than 70 where no quality degradation is visually observed, and for larger than 80 the performance degrades linearly with respect to the quality index. Experiments with H.264/AVC transcoding show that there is no significant performance loss (up to −1%) with CRF30 while the total size of video files is reduced to 30%. In summary, the video quality doesn’t have a large impact on the performance of action recognition models unless the quality degradation is severe and visible. This enables us to transcode the training and validation videos and reduce the file sizes to one-third of the original videos.

The demand for mobile video streams is constantly increasing. With this demand comes a need for mobile devices to receive more videos at ever increasing quality. However, due to the large size of video data and intensive computational requirements, video streaming requires frequent memory access that consume a substantial amount of mobile device power; as a result, the battery life of mobile devices is limited. In this paper, we present a video content-adaptable Region-of-Interest (ROI)-aware video storage technique that promotes power savings. During the video encoding process on the transmitting server, based on the macroblock variance and ROI characterization, the “macroblocks of interest” are identified and embedded in the encoded bitstream. In the decoding process, a new frame buffer with dynamic power-quality trade-off is presented to adapt to the macroblock characteristics during run-time. Results from the system-level and circuit-level simulations show that the proposed technique enables substantially more truncated bits and significant power savings while delivering similar or better video quality as compared to other state-of-the-art solutions.

with the rapid spread of the use of video files cross the world before and during the pandemic, and the availability of capabilities and tools to create, store and transmit video files of high quality. The latest studies illustrate that video files are the highest data types that had been transmitted over the internet, during video transmission or broadcast from one point to the other or over the internet, videos may be damaged for several reasons, such as packet loss or jitter, in this paper, a proposed approach of Ridgelet transformation is presented to improve received video file quality, based on embedded watermark quality present in video frames. It is implemented by extracting three-color channels of video frames, i.e., Red (R), Green (G), and Blue (B). A selected watermark is included in blue channel (B) along with transmitting the video file from source to destination. However, the quality of watermark is measured with execution regarding watermark. Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) were adopted as measurement criteria. The evaluation results show interesting comparative values without noise, in comparison to applying salt, pepper, and Gaussian noise to video frames.

With the widespread usage of video capture devices and social media videos, videos are dominating the multimedia landscape. There is an emerging need for video quality assessment (VQA) that forms the backbone of advanced video systems. Night-time videos play an important role in user capturing, hence being able to accurately assess their quality is critical. However, the characteristics of night-time videos differ from those of general in-capture videos; and VQA algorithms that have been developed for general-purpose videos cannot accurately assess the quality of night-time videos. Research is needed to gain a better understanding of how humans perceive the quality of night-time videos, and use this new understanding to develop reliable VQA algorithms. To this end, we construct a large-scale night-time VQA database, namely Mobile In-capture Night-time Database for Video Quality (MIND-VQ), containing 1181 night-time videos, 435 subjects, and over 130000 opinion scores. We perform thorough analyses to reveal subjective quality assessment behaviors of night-time videos. Furthermore, we propose a new VQA model, namely Visibility-based Night-time Video Quality Assessment Network, VINIA. Spatial and temporal visibility-aware components are characterized to reflect properties of human perception of night-time VQA task. A series of experiments are conducted to compare our VINIA with other existing VQA algorithms using our new MIND-VQ database and other public VQA databases. Experimental results show that our subjective VQA database provides new insights and our new VINIA model achieves superior performance in accessing night-time video quality. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to evaluate the quality of videos on YouTube that demonstrate the intravitreal injection (IVI) procedure as an educational tool., Material and Methods: A search on YouTube using the keywords "intravitreal injection", "intravitreal injection procedure", "eye injection", "eye injection procedure", "dexamethasone intraocular injection", and "anti-VEGF injection" was performed on January 10, 2023. Of the first 300 videos obtained, 70 met the inclusion criteria. The videos were evaluated for content and quality using the IVI procedure checklist score, DISCERN, modified Global Quality Score (GQS), Health on the Net Foundation (HON) code, and the Journal of American Medical Association (JAMA) scores. The quality of the videos was also compared with regard to the uploading source, such as a university or training hospital, educational channels, and individual medical doctors or healthcare professionals., Results: The mean IVI procedure checklist score was 4.84±1.58, and 29 videos fulfilled more than 80% of the checklist items, indicating that only 41.4% of the videos conformed to the IVI procedure recommendations. The mean DISCERN, modified GQS, and JAMA benchmark scores were 34.75±10.46, 2.90±1.09, and 2.09±0.72, respectively, indicating poor overall video quality. The mean HON code score was 4.68±1.39, indicating moderate overall video quality. Videos uploaded by educational channels seem to be of better quality than those uploaded by others., Conclusions: The majority of evaluated YouTube videos on the IVI procedure appear to be of low quality as an educational tool. Videos uploaded by educational channels would be preferred to gain quality information about the IVI procedure., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

With increasing numbers of laparoscopic hysterectomies, surgical trainees are compelled to learn more about endoscopy. Owing to coronavirus disease-related social distancing requirements, online education has gained prominence. Here, we aimed to investigate the laparoscopic hysterectomy video quality on YouTube using the LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS). YouTube was searched on June 7, 2020 using 'laparoscopic hysterectomy'. Three examiners evaluated videos using Global Operative Assessment of Laparoscopic Skills (GOALS). Subsequently, videos were assessed for their conformity to the LAP-VEGaS and LAP-VEGaS Video Assessment Tool. Interobserver reliability was estimated using intraclass coefficients and Cronbach's alpha. Cochran's Q test was used to determine correlations among quantitative data. The median GOALS score was 21.50. The observers' GOALS scores were significantly correlated. The results showed low conformity to the LAP-VEGaS. YouTube is the most used platform among trainees. The low YouTube video educational quality highlights the necessity for peer review, as trainees increasingly seek such resources during the pandemic. What is already known on this subject? YouTube is the most commonly used online resource for educational material among surgical trainees. Online videos usually do not undergo a peer-review process. The LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS) may be used to assess the educational quality of surgical videos. What do the results of this study add? To our knowledge, this is the first study on the quality of laparoscopic hysterectomy videos available on YouTube and the first study to evaluate YouTube laparoscopic surgery videos using the LAP-VEGaS Video Assessment Tool (VAT). Our study revealed the low educational quality of YouTube laparoscopic hysterectomy videos. The LAP-VEGaS VAT seems to be a valid and practical tool for assessing online laparoscopic hysterectomy videos. What are the implications of these findings for clinical practice and/or further research? Medical communities, especially tertiary care or academic centres, may upload educational peer-reviewed videos for trainees seeking this type of resource, especially during the coronavirus disease pandemic, as surgical education alternatives are limited. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the quality of information available in YouTube videos on the treatment of uterine fibroids.Materials and Methods: The DISCERN Scale Criterion was used to quantify the quality of YouTube videos on uterine fibroid embolization. The Video Power Index was used to quantify the popularity of videos on uterine fibroid embolization.Results: Among the 31 videos identified in the study, the average total DISCERN score was 48.82 ± 14.48, indicative of average to poor quality. There was no correlation between a video's popularity and its quality. Popularity, as measured by Video Power Index, was not significantly different between videos containing a board-certified physician and those that did not. Videos with a board-certified interventional radiologist had a significantly lower Video Power Index than those without a board-certified physician.Conclusion: YouTube is not currently a high-quality source of information for uterine fibroid treatment options. Physicians should be aware of highly viewed material on YouTube to have informed discussions with patients about their treatment options and address misperceptions. [ABSTRACT FROM AUTHOR]

In this paper, the optimization network video quality objective assessment method based on fuzzy inference (FI) is proposed. Firstly, the experimental system is built to extract the different types of impairment factors. At the same time, many people are arranged to assess the video quality and give the subjective scores. Secondly, the impairment factors are considered and trained by the support vector machine (SVM) to categorize the video quality. So the corresponding impairment factors of network video will also be categorized and the subjective and objective categories can be compared. Thirdly, the impairment factors of different categories are inputted into the fuzzy inference system. Meanwhile, different categories of fuzzy inference steps are simplified, and the specific objective scores are calculated. At last, the proposed method is compared with other methods; the experimental results show that this method can improve the accuracy. This paper has two contributions: 1. The video quality can be classified by SVM, and the objective scores can be calculated by FI. In addition, different categories of fuzzy inference steps are invented to simplify the inference process.2. Compared with other objective methods, the proposed method can improve the similarity between the subjective and objective scores. This paper will give the detail analysis of them. [ABSTRACT FROM AUTHOR]

IP networks for live TV production have unique requirements such as the ultra high bandwidth and the high sensitivity to packet loss that causes video impairments. Existing multicast protocols are not bandwidth-aware and could cause links to over-subscribe leading to packet loss and negative user quality of experience. Existing video quality error detection tools are reactive by design with no insights into the video domain. In this paper, we introduce MediaFlow, a system for bandwidth aware multicast routing, active in-network detection of video errors, and proactive recovery. MediaFlow utilizes a novel greedy online multicast routing algorithm for efficient routing and admission control. It also introduces novel per-flow video quality metric utilizing unique switch ASIC capabilities for scalable in-network video quality monitoring and rerouting. We implement MediaFlow using data center switches and our testing results confirm that MediaFlow algorithm increases fabric capacity up to 60% compared to state of art multicast routing. MediaFlow can detect errors in video flow integrity at a granularity of 100 mSec at line rate for thousands of flows. The system can proactively recover impacted flows within 1 sec. MediaFlow increases video detection and recovery scale by a thousandfold compared to network edge solutions. [ABSTRACT FROM AUTHOR]

YouTube®, attracts billions of monthly viewers, including those seeking health-related content. However, the quality standards of information are highly variable. The study aimed to evaluate the educational merit of YouTube® concerning pacemakers, focusing on quality and reliability for educating both patients and physicians. The term "pacemaker" was searched on YouTube®. Following the application of exclusion criteria based on video language, duration, and minimum view count, a total of 71 videos were analyzed. Quality was assessed using the Global Quality Score (GQS), while reliability was evaluated using the modified DISCERN (mDISCERN) score. Data and metrics available regarding the channel and the specific video were obtained. The Kolmogorov–Smirnov test was employed to assess data normality, and the Mann-Whitney U test was utilized to determine statistical significance. Youtube videos on cardiac pacemakers proved to be of moderate quality, with an average GQS score of 3.10, and of moderate reliability, indicated by a mean mDISCERN score of 3.08. Higher scores were reported for videos of more than five minute duration, the ones that were targeted at physicians, and the videos with higher view ratios. The presence of a board-certified MD yielded statistically greater mean GQS, but not mDISCERN score. No statistical difference was observed based on the number of likes. In conclusion, while YouTube® offers significant education opportunities, there is a clear need for enhanced oversight and quality control. Healthcare providers should guide patients towards valid resources and consider collaborating with platforms to develop content standards. • Evaluated YouTube's role in pacemaker patient and physician education. • Analyzed over 70 videos using modified DISCERN and Global Quality Score. • Identified quality disparities between videos targeted at patients vs. physicians. • Found significant correlation between video quality and reliability scores. • Provided insights into improving online medical information standards. [ABSTRACT FROM AUTHOR]

The paper presents the results of subjective and objective quality assessments of H.264-, H.265-, and VP9-encoded video. Most of the literature is devoted to subjective quality assessment in well-defined laboratory circumstances. However, the end users usually watch the films in their home environments, which may be different from the conditions recommended for laboratory measurements. This may cause significant differences in the quality assessment scores. Thus, the aim of the research is to show the impact of environmental conditions on the video quality perceived by the user. The subjective assessment was made in two different environments: in the laboratory and in users’ homes, where people often watch movies on their laptops. The video signal was assessed by young viewers who were not experts in the field of quality assessment. The tests were performed taking into account different image resolutions and different bit rates. The research showed strong correlations between the obtained results and the coding bit rates used, and revealed a significant difference between the quality scores obtained in the laboratory and at home. As a conclusion, it must be underlined that the laboratory tests are necessary for comparative purposes, while the assessment of the video quality experienced by end users should be performed under circumstances that are as close as possible to the user’s home environment.

Video delivered over IP networks in real-time applications, which utilize RTP protocol over unreliable UDP such as videotelephony or live-streaming, is often prone to degradation caused by multiple sources. The most significant is the combined effect of video compression and its transmission over the communication channel. This paper analyzes the adverse impact of packet loss on video quality encoded with various combinations of compression parameters and resolutions. For the purposes of the research, a dataset containing 11,200 full HD and ultra HD video sequences encoded to H.264 and H.265 formats at five bit rates was compiled with a simulated packet loss rate (PLR) ranging from 0 to 1%. Objective assessment was conducted by using peak signal to noise ratio (PSNR) and Structural Similarity Index (SSIM) metrics, whereas the well-known absolute category rating (ACR) was used for subjective evaluation. Analysis of the results confirmed the presumption that video quality decreases along with the rise of packet loss rate, regardless of compression parameters. The experiments further led to a finding that the quality of sequences affected by PLR declines with increasing bit rate. Additionally, the paper includes recommendations of compression parameters for use under various network conditions.

In QoE fairness optimization of multiple video streams, a distributed video stream fairness scheduling strategy based on federated deep reinforcement learning is designed to address the problem of low bandwidth utilization due to unfair bandwidth allocation and the problematic convergence of distributed algorithms in cooperative control of multiple video streams. The proposed strategy predicts a reasonable bandwidth allocation weight for the current video stream according to its player state and the global characteristics provided by the server. Then the congestion control protocol allocates the proportion of available bandwidth, matching its bandwidth allocation weight to each video stream in the bottleneck link. The strategy trains a local predictive model on each client and periodically performs federated aggregation to generate the optimal global scheme. In addition, the proposed strategy constructs global parameters containing information about the overall state of the video system to improve the performance of the distributed scheduling algorithm. The experimental results show that the introduction of global parameters can improve the algorithm's QoE fairness and overall QoE efficiency by 10% and 8%, respectively. The QoE fairness and overall QoE efficiency are improved by 8% and 7%, respectively, compared with the latest scheme. [ABSTRACT FROM AUTHOR]

Tele-training in surgical education has not been effectively implemented. There is a stringent need for a high transmission rate, reliability, throughput, and reduced distortion for high-quality video transmission in the real-time network. This work aims to propose a system that improves video quality during real-time surgical tele-training. The proposed approach aims to minimise the video frame's total distortion, ensuring better flow rate allocation and enhancing the video frames' reliability. The proposed system consists of a proposed algorithm for Enhancing Video Quality, Distorting Minimization, Bandwidth efficiency, and Reliability Maximization called (EVQDMBRM) algorithm. The proposed algorithm reduces the video frame's total distortion. In addition, it enhances the video quality in a real-time network by dynamically allocating the flow rate at the video source and maximizing the transmission reliability of the video frames. The result shows that the proposed EVQDMBRM algorithm improves the video quality with the minimized total distortion. Therefore, it improves the Peak Signal to Noise Ratio (PSNR) average by 51.13 dB against 47.28 dB in the existing systems. Furthermore, it reduces the video frames processing time average by 58.2 milliseconds (ms) against 76.1, and the end-to-end delay average by 114.57 ms against 133.58 ms comparing to the traditional methods. The proposed system concentrates on minimizing video distortion and improving the surgical video transmission quality by using an EVQDMBRM algorithm. It provides the mechanism to allocate the video rate at the source dynamically. Besides that, it minimizes the packet loss ratio and probing status, which estimates the available bandwidth. [ABSTRACT FROM AUTHOR]

Medical education instructional videos are more popular and easier to create than ever before. Standard quality measures for this medium do not exist, leaving educators, learners, and content creators unable to assess these videos. Drawing from the literature on video quality and popularity, reusable learning objects, and multimedia and curriculum development principles, we developed a 26-item instructional video quality checklist (IVQC), to capture aspects of educational design (six items), source reliability (four items), multimedia principle adherence (10 items), and accessibility (six items). Two raters applied IVQC to 206 videos from five producers across topics from two organ systems (cardiology and pulmonology) encompassing four disciplines (anatomy, physiology, pathology, and pharmacology). Inter-rater reliability was strong. According to two-rater means, eight multimedia items were present in over 80% of videos. A minority of videos included learning objectives (46%), alternative language translations (41%), when the video was updated (40%), analogies (37%), or references (9%). Producer ratings varied significantly (p <.001) across 17 of 26 items. There were no significant differences according to the video topic. IVQC detected differences in elements of instructional video quality. Future work can apply this instrument to a broader array of videos and in authentic educational settings. [ABSTRACT FROM AUTHOR]

The popularity of video on-demand streaming services increased tremendously over the last years. Most services use http-based adaptive video streaming methods. Today's movies and TV shows are typically recorded in UHD-1/4K and streamed using settings attuned to the end-device and current network conditions. Video quality prediction models can be used to perform an extensive analysis of video codec settings to ensure high quality. Hence, we present a framework for the development of pixel-based video quality models. We instantiate four different model variants (hyfr, hyfu, fume and nofu) for short-term video quality estimation targeting various use cases. Our models range from a no-reference video quality model to a full-reference model including hybrid model extensions that incorporate client accessible meta-data. All models share a similar architecture and the same core features, depending on their mode of operation. Besides traditional mean opinion score prediction, we tackle quality estimation as a classification and multi-output regression problem. Our performance evaluation is based on the publicly available AVT-VQDB-UHD-1 dataset. We further evaluate the introduced center-cropping approach to speed up calculations. Our analysis shows that our hybrid full-reference model (hyfr) performs best, e.g. 0.92 PCC for MOS prediction, followed by the hybrid no-reference model (hyfu), full-reference model (fume) and no-reference model (nofu). We further show that our models outperform popular state-of-the-art models. The introduced features and machine-learning pipeline are publicly available for use by the community for further research and extension.