Video Processing
H. Roodaki
Abstract
Background and Objectives: From the multiview recorded video, free viewpoint video provides flexible viewpoint navigation. Thus, a lot of views need to be sent to the receivers in an encoded format. The scalable nature of the coded bitstream is one method of lowering the volume of data. However, adhering ...
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Background and Objectives: From the multiview recorded video, free viewpoint video provides flexible viewpoint navigation. Thus, a lot of views need to be sent to the receivers in an encoded format. The scalable nature of the coded bitstream is one method of lowering the volume of data. However, adhering to the limitations of the free viewpoint application heavily relies on the kind of scalable modality chosen. The perceptual quality of the received sequences and the efficiency of the compression technique are significantly impacted by the scalable modality that was chosen. Methods: In order to address the primary issues with free-viewpoint video, such as high bandwidth requirements and computational complexity, this paper suggests a scalable framework. The two components of the suggested framework are as follows: 1) introducing appropriate scalable modality and data assignment to the base and enhancement layers; and 2) bit budget allocation to the base and enhancement layers using a rate control algorithm. In our novel scalable modality, termed Tile-based scalability, the idea of Region of Interest (ROI) is employed, and the region of interest is extracted using the tile coding concept first presented in the MV-HEVC.Results: When compared to the state-of-the-art techniques, our approach's computational complexity can be reduced by an average of 44% thanks to the concept of tile-coding with parallel processing capabilities. Furthermore, in comparison to standard MV-HEVC, our suggested rate control achieves an average 17.7 reduction in bandwidth and 1.2 improvement in video quality in the Bjøntegaard-Bitrate and Bjøntegaard-PSNR scales.Conclusion: Using new tile-based scalability, a novel scalable framework for free-viewpoint video applications is proposed. It assigns appropriate regions to the base and enhancement layers based on the unique features of free viewpoint scalability. Next, a rate control strategy is put forth to allocate a suitable bitrate to both the base and enhancement layers. According to experimental results, the suggested method can achieve a good coding efficiency with significantly less computational complexity than state-of-the-art techniques that used the λ-domain rate control method.
Video Processing
A. Akbari; H. Farsi; S. Mohamadzadeh
Abstract
Background and Objectives: Video processing is one of the essential concerns generally regarded over the last few years. Social group detection is one of the most necessary issues in crowd. For human-like robots, detecting groups and the relationship between members in groups are important. Moving in ...
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Background and Objectives: Video processing is one of the essential concerns generally regarded over the last few years. Social group detection is one of the most necessary issues in crowd. For human-like robots, detecting groups and the relationship between members in groups are important. Moving in a group, consisting of two or more people, means moving the members of the group in the same direction and speed. Methods: Deep neural network (DNN) is applied for detecting social groups in the proposed method using the parameters including Euclidean distance, Proximity distance, Motion causality, Trajectory shape, and Heat-maps. First, features between pairs of all people in the video are extracted, and then the matrix of features is made. Next, the DNN learns social groups by the matrix of features.Results: The goal is to detect two or more individuals in social groups. The proposed method with DNN and extracted features detect social groups. Finally, the proposed method’s output is compared with different methods.Conclusion: In the latest years, the use of deep neural networks (DNNs) for learning and detecting has been increased. In this work, we used DNNs for detecting social groups with extracted features. The indexing consequences and the outputs of movies characterize the utility of DNNs with extracted features.
