基于自适应码率分配的压缩传感深度视频编码方法

doi:10.16451/j.cnki.issn1003-6059.201804001

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Abstract By utilizing the compressive sensing in the depth video, the compressive sensing depth video(CSDV) is obtained. However, the redundancy of CSDV is still huge. A coding scheme for compressive sensing depth video(CSDV) based on Gaussian mixture models(GMM) and object edges is proposed. Firstly, the compressive sensing(CS) is utilized to compress 8 depth frames to acquire a CSDV frame in the temporal direction. A whole CSDV frame is divided into a set of non-overlap patches, and object edges in the patches are detected by Canny operator to reduce the computational complexity of quantization. Then, variable bits for different patches are allocated based on the percentages of non-zero pixels in every patch. The GMM is employed to model the CSDV frame patches and design product vector quantizers to quantize CSDV frames.

Key words： Gaussian Mixture Model Depth Video Coding Compressive Sensing View Synthesis

Received: 01 January 2018

ZTFLH:

TP 391

Fund:Supported by Key Projects of Tri-Co-Robots Plan of National Natural Science Foundation of China(No.91748208), National Natural Science Foundation of China(No.61573268), National Key R&D Program of China(2016YFB1000903)

Corresponding Authors: LAN Xuguang(Corresponding author), Ph.D., professor. His research interests include computer vision, deep learning and pattern recognition.

About author:: WANG Kang, master student. His research interests include compressive sensing and depth video;LI Xiangwei, Ph.D., researcher. His research interests include image/video proce-ssing, compressive sensing and scalable video coding.

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Cite this article:

WANG Kang,LAN Xuguang,LI Xiangwei. Coding Scheme for Compressive Sensing Depth Video Based on Adaptive Bits Allocation[J]. , 2018, 31(4): 293-299.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201804001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I4/293

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