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
achieve remarkable RD-performance gain, with lower sampling rate, compared to
applying the quantization model on the standard CS.