A new non-uniform quantization method based on distribution of compressive sensing measurements and coefficients discarding

Compressive sensing (CS) is a new method of sampling and compression which has great advantage over previous signal compression techniques. However, its compression ratio is relatively low compared with most of the current coding standards, which means a good quantization method is very important for CS. In this paper, a new method of non-uniform quantization is proposed based on the distribution of CS measurements and coefficients discarding. Firstly, the magnitude of CS measurements is estimated and the low probability measurements are discarded because of their high quantization error. It should be noted that the dropped measurements almost take no effect on the recovery quality because of the equal-weight property of CS samples. Then a nonlinear quantize function based on the distribution of sensed samples is proposed, by which those remained measurements are quantized. The experimental results show that the proposed method can obviously improve the quality of reconstructed image compared with previous methods in terms of the same sampling rate and different reconstruction algorithms.