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Objective To evaluate the information quality of hypertension-related videos on the TikTok short video platform and assess their potential value for public health education. Methods In June 2024, we searched the TikTok short video platform using the keyword "hypertension" selecting the top 200 videos and ultimately including 177 videos for quality evaluation. Structured content integrity assessment tools and the DISCERN video quality assessment tool were used to evaluate the videos. The correlation between video quality and characteristics (duration, likes, comments, favorites, and shares) was studied. Results The 177 videos received 11 250 432 likes, 366 588 comments, 3 265 929 favorites, and 3 780 513 shares. The median video length was 111 seconds (74, 158), and the median upload days were 672 days (147, 878). 89.3% of the videos were posted by doctors, 8.5% by individual science communicators, and 2.3% by news agencies. 98.9% of the videos had subtitles, all featured individuals, and only 26.0% included animated demonstrations. Content integrity scores were: definition 0.19±0.40, symptoms 0.11±0.32, risk factors 0.29±0.50, assessment 0.36±0.45, management 0.81±0.47, and outcomes 0.63±0.58. 90.4% of the videos had a DISCERN score of ≤50, indicating "average" or below quality, with no significant difference in DISCERN scores among different publishers (P>0.05). DISCERN scores were significantly correlated with video duration (r=0.409, P<0.001). Conclusion The overall quality of hypertension-related videos on TikTok is low, with a significant positive correlation between video duration and quality. High-quality videos receive less attention, making TikTok an unsuitable platform for hypertension education due to low information quality and reliability. [ABSTRACT FROM AUTHOR]

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

This paper proposed a distributed video streaming fair scheduling strategy based on federated deep reinforcement learning to address the issues of unfair user QoE and low bandwidth utilization caused by uneven video bandwidth allocation in bottleneck links. This strategy dynamically generated bandwidth allocation weights based on the client's network status and the QoE level of each video stream. The congestion control algorithm at the server side allocated bandwidth to each video stream in the bottleneck link according to the computed weights, ensuring equitable transmission of video streams in the bottleneck link. Each video terminal operated a bandwidth allocation agent, and multiple agents train periodically using federated learning to facilitate rapid convergence of the agent models. The bandwidth allocation agents synchronized their training parameters through a consensus mechanism, enabling distributed aggregation of the agent model parameters while ensuring the security of parameter sharing. Experimental results demonstrate that the proposed strategy improves QoE fairness and overall QoE efficiency by 10% and 7%, respectively, compared to the latest solutions. This indicates that the proposed strategy has potential and effectiveness in addressing the uneven allocation of video stream bandwidth and improving user experience. [ABSTRACT FROM AUTHOR]

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.