关键词: 压缩感知, 最佳量化, 压缩视频感知, 差分脉码调制

Abstract: Compressed Video Sensing（CVS） is a video encoding method, which combines Compressed Sensing（CS） with Distributed Video Coding（DVC）. It is also called Distributed Video Compressed Sensing. In CVS, each frame of video is subjected to block partitioning, compression sampling, and then DPCM is performed on the data. Finally, quantization is performed using uniform or non-uniform quantization. At present, the design of the CVS quantizer is mostly designed on the premise that the sampled data or residual data obey the Gaussian distribution. This paper analyzes the distribution features of the data after image block division, compressed sampling, and DPCM by Kolmogorov-Smirnov test. On the basis of those analysis, Lloyd’s optimal quantizer design criteria is used in the proposed quantizer. Experimental results show that proposed quantizer has reduced about 14.2% in BD-Rate and improved about 0.11?dB in BDPSNR compared to the traditional quantizing method, improves the compression efficiency and reconstruction quality of CVS.

Key words: compressed sensing, optimal quantizer, Compressed Video Sensing（CVS）, Differential Pulse Code Modulation（DPCM）