Residual distributed compressive video sensing
残差分布式视频压缩感知*

Compressive sensing (CS) is a new theory which states that the compression can be achieved in the process of data acquisition, gives strong supports for simplifying the acquisition algorithms. Meanwhile, DVC provides a basis for low-complexity video coding. Hence,this paper addressed the problem of low-complexity video coding by integrating the respective characteristic of DVC and CS.It proposed a new residual distributed compressive video sensing (RDCVS) using the residual coding technology. At the encoder,it just encoded each key frame by traditional intra-frame model and measured the other non-key frames randomly based on a residual joint sparse model. At the decoder,it implemented the efficient reconstruction using side information and the modified GPSR (gradient projection for sparse reconstruction). Some computation-consuming algorithms, such as motion estimation and transform coding, are moved to the decoder, the low-complexity encoding is therefore preserved in RDCVS. The experimental results show that RDCVS achieves 23 dB improvements in PSNR compared to the referenced scheme.