Joint Sampling Rate and Quantization Rate-Distortion Analysis in 5G Compressive Video Sensing

Compressed video sensing (CVS) is one of the 5G application of compressed sensing (CS) to video coding. Block-based residual reconstruction is used in CVS to explore temporal redundancy in videos. However, most current studies on CVS focus on random measurements without quantization, and thus they are not suitable for practical applications. In this study, an efficient rate-control scheme combining measurement rate and quantization for residual reconstruction in CVS is proposed. The quantization effects on CS measurements and recovery for video signals are first analyzed. Based on this, a mathematical relationship between quantitative distortion (QD), sampling rate (SR), and the quantization parameter (QP) is derived. Moreover, a novel distortion model that exhibits the relationship between QD, SR, and QP is presented, if statistical independency between the QD and the CS reconstruction distortion is assumed. Then, using this model, a rate-distortion (RD) optimized rate allocation algorithm is proposed, whereby it is possible to derive the values of SR and QP that maximize visual quality according to the available channel bandwidth.