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The point cloud provides viewers with intuitive geometric understanding but requires a huge amount of data. Moving Picture Experts Group (MPEG) has developed video-based point-cloud compression in the range of 300-700. As the compression rate increases, the complexity increases to the extent that it takes 101.36 s to compress one frame in an experimental environment using a personal computer. To realize real-time point-cloud compression processing, the direct patch projection (DPP) method proposed herein simplifies the complex patch segmentation process by classifying and projecting points according to their geometric positions. The DPP method decreases the complexity of the patch segmentation from 25.75 s to 0.10 s per frame, and the entire process becomes 8.76 times faster than the conventional one. Consequently, this proposed DPP method yields similar peak signal-to-noise ratio (PSNR) outcomes to those of the conventional method at reduced times (4.7-5.5 times) at the cost of bitrate overhead. The objective and subjective results show that the proposed DPP method can be considered when low-complexity requirements are required in lightweight device environments.

An image captured by a single camera has a smaller viewing angle than that of the human eye. One method to expand this viewing angle is a technique known as image stitching, which generates a wider view from images captured with multiple cameras. Although this technique has found uses in multiple industries, it is vulnerable to parallax distortion, wherein objects disappear from or repeatedly appear in stitched images when the parallax between cameras differs significantly. To minimize parallax distortion, seam-based and multi-homography-based methods have been proposed. In particular, the seam-based method enables faster image stitching owing to its intuitive procedure; however, the seam generation matrix may still incur parallax distortion under certain restrictive circumstances, and a longer stitching time is required when this method is applied to video sequences. This motivated us to develop the Guided Energy–Depth Map, which uses the energy function, depth information, and guidance map to minimize parallax distortion from a human visual perspective and reduce the time required to apply the stitching process to video sequences. Based on Average Seam Error (ASE) evaluation, the proposed method produces better seams than energy functions in 25 out of 32 experimental datasets, and the improvement rate of ASE evaluation is 15.58%. Also, the Frame Selection module for video stitching proposed in this paper takes only 7.27% of the time to find a specific frame for seam regeneration compared to the instance segmentation-based frame selection method.

A point cloud acquired through a Light Detection And Ranging (LiDAR) sensor can be illustrated as a continuous frame with a time axis. Since the frame-by-frame point cloud has a high correlation between frames, a higher compression efficiency can be obtained by using an inter-prediction scheme, and for this purpose, Geometry-based Point Cloud Compression (G-PCC) in the Moving Picture Expert Group (MPEG) opened Inter-Exploratory Model (Inter-EM) which experiments on continuous LiDAR based point cloud frames compression through inter-prediction. The points of the LiDAR based point cloud have two different types of motion: global motion brought about by a vehicle with a LiDAR sensor and local motion generated by an object e.g., a walking person. Thus, Inter-EM consists of a compression structure in terms of both global and local motion, and the Inter-EM’s global motion compensation technology increases the compression efficiency via a single matrix describing the global motion of points. However, this is difficult to predict with a single matrix, which causes imprecise global motion estimation since the objects in a LiDAR-based point cloud show different global motion estimates according to object characteristics such as shape and position. Therefore, this paper proposes a global motion prediction and compensation scheme that considers the characteristics of objects for efficient compression of LiDAR-based point cloud frames. The proposed global motion prediction and compensation scheme achieved maximum gain of −22.0% and average of −9.4% in terms of the Bjontegaard-Delta-rate (BD-rate), and effectively compressed the LiDAR-based sparse point cloud.

Advanced Television Systems Committee (ATSC) 3.0 is a more innovative standardization including Ultra High Definition (UHD) broadcasting and mobile High-Definition (HD) broadcasting. It also includes the deployment of a disaster information alarm function as well as the adoption of All-Internet Protocol (IP) based transmission in terrestrial broadcasting networks. Thus, ATSC enables additional technologies such as hybrid broadcasting services by interworking terrestrial broadcasting and internet networks for bidirectional customized services. In addition, there is a growing interest in wider viewing contents, which provide more immersive contents than existing UHD broadcasting mediums. Wider viewing contents can be acquired by using large field of view (FoV) lenses such as fish-eye lenses or by stitching several sub-images. The images captured by large FoV lenses have heavily curved surface distortions, while the distortion of the stitching-based wider viewing contents caused by the synthesizing process on overlapping regions is lower. Provision of wider viewing contents over the traditional broadcasting service has limitations in terms of bandwidth. However, they can be overcome by using hybrid broadcasting platforms such as ATSC 3.0, which transmits a video sequence over an RF channel and other viewing-angle video sequences through the Internet network, where signalling among those video sequences is required. Therefore, this paper proposes a hybrid broadcasting platform for transmitting multiple video sequences over broadcasting and Internet networks, and stitching them for wider viewing.

Hybrid broadcasting utilizing heterogeneous networks can provide not only Audio/Video(A/V)-based unidirectional broadcast services but also various interactive A/V and data services via both broadcast and communication networks. Customers of hybrid broadcasting services can then consume A/V content that is transmitted from broadcasting stations and data services relevant to A/V content through communication networks, such as the Internet. Since interactive data can be widely ingested in various countries and regions, it is advantageous for it to be customized for personalized services depending on consumer preferences and locations. However, the current broadcasting standard has not yet defined a standardized method of inserting content into a broadcasting program. Thus, the research on content insertion technology using heterogeneous networks is actively progressing. The most important technical challenge of inserting content is rendering a smooth replacement of on-air broadcast content with media data through communication networks and an accurate return to the over-the-air broadcast content consistent with the rendering timeline using legacy receivers. Since most legacy receivers have one decoder, both the inserted content and the broadcast content have to be handled by one decoder. When two video streams are processed by one decoder, decoding and rendering problems due to the buffer overflow may occur, and buffer management of the decoder is required to solve this problem. Therefore, this paper proposes a content insertion method of using a frame control-based receiver buffer model to enable smooth and accurate rendering of inserted and on-air content without any blackout scenes.

Point cloud content is widely used to store and represent 3D volumetric objects with a complex and detailed representation from any direction of view. However, the amount of data needed for point cloud content is much larger than that of 2D representations. To overcome this difficulty, MPEG has started to develop a Video-based Point Cloud Compression (V-PCC) that is designed by projecting point cloud content into 2D content and compressing the 2D content using conventional 2D video codecs. Compression efficiency of the V-PCC can be achieved when 3D motion flow and textual conformity on 3D surfaces are preserved through 2D projections that are favorable to the 2D video codec. As mentioned above, point cloud content has a complex geometry, therefore when decomposing a point from 3D coordinates to construct a 2D patch, several situations must be considered in addition to the location of adjacent points. This paper addresses the issues in such complex geometry by proposing a method that preserves 3D homogeneity in 2D patches. Comprehensive experiments are conducted to demonstrate bitrate savings of 0.5%, 0.6%, 7.8%, 7.0% and 5.5% in random access mode and 0.1%, 0.0%, 7.0%, 4.2% and 3.3% in all intra mode for D1, D2, Y, Cb, and Cr, respectively, compared to the reference software.

A point cloud visualizes information by placing a voxel with a color value and a position value in a three-dimensional space. Since a point cloud uses hundreds of thousands or millions of points to visualize information, a large number of bits is needed compared to existing 2D media. Therefore, it is essential to compress point data for transmission and storage. The Moving Picture Expert Group (MPEG) is developing a point cloud compression method based on 2D video that takes advantage of the benefits of coding efficiency and the wide adaption of video codecs by various industries. This compression method is called video-based point cloud compression (V-PCC). Generally, video codecs use a compression method that employs a block matching algorithm. Currently, V-PCC is conducted using 2D video codecs, which means that motion information used by V-PCC is obtained from 2D video sequences. Thus, this 2D-based motion information limits the characterization of the motion in terms of 3D-points, which is also disadvantageous to compression efficiency. In this paper, we propose a method for estimating and compensating the motion in terms of a 3D object when compressing a dynamic object point cloud using a conventional video codec. The proposed 3D motion estimation and compensation technology showed higher gain overall in terms of BD-rate and was proven to effectively compress 3D point cloud content on the basis of 3D motion.

From stitching video sequences point of view, an existing image stitching technique may have limitations on object movement or loss in the stitched video sequence due to the different parallax of the individual video sequences. Also, an existing image stitching technique have higher computational complexity and have matching accuracy limitations in obtaining descriptors for many factors. Therefore, this paper proposes an edge-based weight minimum error seam method in order to overcome the limitations caused by parallax and video stitching and trigonometric ratio-based image match algorithm in order to reduce computational complexity and insensitivity to rotation, illumination, image size, or resolution.

Extensive research has been conducted in the past to determine age, gender, and words spoken in Bangla speech, but no work has been conducted to identify the regional language spoken by the speaker in Bangla speech. Hence, in this study, we create a dataset containing 30 h of Bangla speech of seven regional Bangla dialects with the goal of detecting synthesized Bangla speech and categorizing it. To categorize the regional language spoken by the speaker in the Bangla speech and determine its authenticity, the proposed model was created; a Stacked Convolutional Autoencoder (SCAE) and a Sequence of Multi-Label Extreme Learning machines (MLELM). SCAE creates a detailed feature map by identifying the spatial and temporal salient qualities from MFEC input data. The feature map is then sent to MLELM networks to generate soft labels and then hard labels. As aging generates physiological changes in the brain that alter the processing of aural information, the model took age class into account while generating dialect class labels, increasing classification accuracy from 85% to 95% without and with age class consideration, respectively. The classification accuracy for synthesized Bangla speech labels is 95%. The proposed methodology works well with English speaking audio sets as well.

The objective of digital broadcasting has evolved from providing a plain video service to offering a realistic visual experience. Technologies such as 3DTV and UHDTV have been suggested to achieve this new objective by providing an immersive and stereoscopic visual experience. However, owing to the high bandwidth requirements of such services, the broadcasting industry has faced a challenge to find a new transport mechanism for overcoming the bandwidth limitation. The standardization organizations, the Advanced Television Systems Committee, Digital Video Broadcasting, and Telecommunications Technology Association, have been working on the integration of broadcasting and a broadband network (IP) to resolve the bandwidth issue of realistic video services. This paper introduces a frame-level timeline synchronization and transport system target decoder model for providing a stable 3DTV service over a hybrid network. The experimental results indicate that the proposed technologies can be successfully adopted as a reference model in a broadcast-broadband hybrid 3DTV service and other IP-associated hybrid broadcasting services. [ABSTRACT FROM AUTHOR]