Model-based quantization for perceptually weighted compressed video sensing

Abstract

Exploiting perceptual-based weighting can improve the reconstruction quality for compressed video sensing (CVS). Nevertheless, practical implementation of the compressed sensing (CS) requires quantizing the measurements. We propose a simplified sampling rate model for the perceptual CVS to achieve compromise between the number of measurements and the quantization bit-depth, which are the main contributing factors in the CS rate-distortion (RD) performance. The proposed model can achieve near optimal RD-performance obtained through exhaustive simulations. In addition, simulation results show that the quantized perceptual CVS achieves remarkable RD-performance gain, with lower sampling rate, compared to applying the quantization model on the standard CS.