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A video compression artifact reduction approach combined with quantization parameters estimation

JOURNAL OF SUPERCOMPUTING 2022年第11期78卷 13564-13582页

作者： Shuai, Xin Qing, Linbo Zhang, Mozhi Sun, Weiheng He, Xiaohai Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Univ Maryland Dept Comp Sci College Pk MD 20742 USA

High Efficiency Video Coding is one of the most widely used Video Coding standards. It could encode videos to bitstream with a high compression rate for transporting, and videos would be reconstructed by decoding bitstream. However, decoded videos with compression artifacts would degrade the viewing experience of users. At present, many methods based on deep learning have been proposed to reduce compression artifacts for quality enhancement. Most methods train their models assuming that quantization parameters (QP) are known. But the QPs of videos are obtained from bitstream, which means QPs are probably unknown if there are only decoded videos. Without QP, training a network for compression artifacts reduction would become difficult. To this end, we propose a video compression artifacts reduction approach combined with quantization parameters estimation (VRQPE). In light of the fact that decoded videos with different QPs have different artifacts, we extract representative sample blocks in video frames to estimate the QPs of these videos. Then, we propose a majority voting to achieve better estimation results. Afterward, we proposed a quality enhancement network to reduce compressed artifacts. Extensive experiments demonstrate that our proposed VRQPE achieves superior performance.

关键词： Video coding Compression artifacts quantization parameters Quality enhancement

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Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

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APPLIED SOFT COMPUTING 2015年 36卷 218-227页

作者： Babu, R. Venkatesh Rangarajan, Badrinarayanan Sundaram, Suresh Tom, Manu Indian Inst Sci Supercomp Educ & Res Ctr Bangalore 560012 Karnataka India Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Human action recognition Compressed domain Motion vectors quantization parameters PBL-McRBFN Meta cognitive learning

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Compressed domain human action recognition in H.264/AVC video streams

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第21期74卷 9323-9338页

作者： Tom, Manu Babu, R. Venkatesh Praveen, R. Gnana Indian Inst Sci SERC Video Analyt Lab Bangalore 560012 Karnataka India

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

关键词： H.264/AVC Human action recognition Compressed domain video analysis Motion vectors quantization parameters

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A Fast Rate Control Algorithm Using Statistical Properties in a Unit-Layer for H.264/AVC Video Coding

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JOURNAL OF INTERNET TECHNOLOGY 2015年第4期16卷 737-746页

作者： Kim, Beomsu Hong, Min-Cheol Soongsil Univ Sch Elect Engn Seoul South Korea

This paper presents a fast rate control algorithm in a unit-layer for H.264/AVC video coding. The statistical relation between the quantization parameter (QP) and the encoded bits of a unit is derived for inter-coded frames. In addition, a new complexity estimation is defined to determine the weights of complexity of frame layer and unit layer. Using the statistical properties and complexity updating process, the number of encoded bits was determined as a function of the QP, leading to a significant computational cost reduction, stable buffer management, and automatic detection of region of interest (ROI) without extra computational cost. Experimental results show the capability of the proposed algorithm.

关键词： H.264/AVC Rate control quantization parameters Complexity estimation Computational cost

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Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Human Action Recognition in Compressed Domain using PBL-McRB...

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9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

作者： Rangarajan, Badrinarayanan Radhakrishnan, Venkatesh Babu Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Indian Inst Sci Supercomp Educ & Res Ctr Bangalore 560012 Karnataka India

ISBN: (纸本)9781479928439

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

关键词： H.264/AVC Human action recognition Compressed domain video analysis Motion vectors quantization parameters Meta-cognitive learning radial basis function network PBL-McRBFN

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Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Human Action Recognition in Compressed Domain using PBL-McRB...

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International Conference on Intelligent Sensors, Sensor Networks and Information Processing

作者： Badrinarayanan Rangarajan Venkatesh Babu Radhakrishnan School of Computer Engineering Nanyang Technological University Supercomputer Education and Research Centre Indian Institute of Science

ISBN: (纸本)9781479928446

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBFN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component (MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PBL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

关键词： H.264/AVC Human action recognition Compressed domain video analysis Motion vectors quantization parameters Meta-cognitive learning Radial basis function network PBL-McRBFN

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Improved strategy of QP decision for hierarchical B pictures structure in SVC

Improved strategy of QP decision for hierarchical B pictures...

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The 2013 3rd International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2013)

作者： Jing Luna Ma Lini Zhang Chaofu Dept.of Computer Science Beijing Information Science & Technology Univ.

ISBN: (纸本)9780819495587

In Scalable Video Coding(SVC) the coding efficiency for hierarchical prediction structures is highly dependent on how to choose the quantization parameters(QP) for pictures of different temporal layers. For pictures that have strong temporal correlation and pictures at low bitrate, large peak SNR(PSNR) fluctuations will be produced when using the strategy presented in the proposal JVT-P014. In order to decrease the fluctuations this paper proposed a new method which combines the persistence of vision to decrease the fluctuations of the video quality. Simulated results show that the proposed technique is efficient. When compared with the method in the proposal JVT-P014, the PSNR difference decreases is about 20%, and it is more effectively for the pictures with strong temporal correlation. The algorithm in this paper provides valuable references for further study of optimizing encoder.

关键词： SVC Hierarchical B pictures quantization parameters

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Research on Multi-View Video Plus Depth Coding Based on Network Bandwidth

Research on Multi-View Video Plus Depth Coding Based on Netw...

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International Workshop on Information and Electronics Engineering (IWIEE) / International Conference on Information, Computing and Telecommunications (ICICT)

作者： Zhu, Yapei MeiYu Zhang, Gunjun Jiang, Gangyi Peng, Zongju Ningbo Univ Fac Informat Sci & Engn Ningbo 315211 Zhejiang Peoples R China

In the free viewpoint television systems, video coder often has to operate within fixed bandwidth limitations over the network. This paper firstly shows detailed coding results at the target bit rates and selects the optimal combinations of quantization parameters (QPs) for video and depth. Then the joint coding for multi-view video plus depth which utilizes the optimal combination of the QPs is proposed. Simulation results demonstrate that the proposed coding method decreases the bit-rate by 16.07%similar to 24.02% in comparison with JMVC, while the proposed method has better performance in optimizing the tradeoff between bit-rate and visual quality. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology

关键词： Free viewpoint television systems Multi-view video coding Network bandwidth quantization parameters

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Research on Multi-View Video Plus Depth Coding Based on Network Bandwidth

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Procedia Engineering 2012年 29卷 2365-2370页

作者： Yapei Zhu Mei Yu Gunjun Zhang Gangyi Jiang Zongju Peng Faculty of Information Science and Engineering Ningbo University Ningbo 315211 China

In the free viewpoint television systems, video coder often has to operate within fixed bandwidth limitations over the network. This paper firstly shows detailed coding results at the target bit rates and selects the optimal combinations of quantization parameters (QPs) for video and depth. Then the joint coding for multi-view video plus depth which utilizes the optimal combination of the QPs is proposed. Simulation results demonstrate that the proposed coding method decreases the bit-rate by 16.07%∼24.02% in comparison with JMVC, while the proposed method has better performance in optimizing the tradeoff between bit-rate and visual quality.

关键词： Free viewpoint television systems Multi-view video coding Network bandwidth quantization parameters

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Joint Coding for Multiview Video plus Depth Based on View Rendering

Joint Coding for Multiview Video plus Depth Based on View Re...

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第七届和谐人机环境联合学术会议（HHME2011)、第20届全国多媒体学术会议（NCMT2011）、第7届全国人机交互学术会议（CHCI2011）、第7届全国普适计算学术会议（PCC2011）

作者： Yapei ZHU 1 , Zongju PENG 1 , Mei YU 1,2 , Feng SHAO 1 , Songyin FU 1 , Ting LUO 1 , Fucui LI 1 , Gangyi JIANG 1,2 1. Ningbo University, Faculty of Information Science and Engineering, Ningbo, 315211 2. Nanjing University, National Key Lab of Software New Technology, Nanjing, 210093

Depth images are widely utilized in multiview video applications to synthesize the virtual views. In this paper, the method for reliability-based view synthesis from compressed multi-view + depth data (MVD) is investigated and corresponding results are shown. For evaluating how the separate coding results influence the view synthesis quality, this paper firstly generates results from the compression artifacts only, where video and depth coding is carried out separately. In the next step, we derive the edges in order to quantify the effect of lossy coding of the non-edges areas in depth maps on synthesized view quality. Finally, the joint coding for multiview video plus depth which utilizes the relationship of the QPs is proposed. Simulation results demonstrate that the proposed coding method decreases the bitrate by 10.91% on average at higher bitrate in comparison with JMVC.

关键词： multiview video coding compression of depth the fidelity of synthesized views quantization parameters

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