DISTRIBUTED COMPRESSIVE VIDEO SENSING BASED ON SMOOTHED L0 NORM WITH PARTIALLY KNOWN SUPPORT
Cong Ma, Yu Liu, Lin Zhang, Xuqi ZhuAbstract
Distributed compressive video sensing (DCVS), aiming at capturing and compressing video data simultaneously, is an emerging field which exploits both intra- and inter-frame correlation. In this paper, we present a new algorithm based on smoothed norm (SL0) which tries to directly minimize the norm to decode a Wyner-Ziv frame when parts of its correlated key frame’s support is known as side information (SI) in a typical DCVS scenario. With the assistance of the modified initialization, our proposed algorithm can reconstruct the Wyner-Ziv frame of the same accuracy with much lower measurement rate compared to the case when the partially known support is not used as SI. It is experimentally shown that our proposed scheme outperforms GPSR at the expense of a tolerable decoding complexity. When compared with modified-cs, a large saving in decoding CPU time is achieved in sacrifice of some PSNR performance.
Read Submission [382]