Compressive Sensing Multi-Layer Residual Coefficients for Image Coding

Abstract

Compressive sensing (CS)-based image coding scheme has been enthusiastically studied, but it still has a poor rate-distortion performance compared with the traditional image coding techniques. In this paper, we propose a CS multi-layer residual coding scheme to rectify this problem to a certain extent. By dividing CS measurements into multi-layers and predicting a particular layer’s measurements with all its preceding layers’ measurements, we can transform CS measurements into multi-layer residual coefficients, which are easier to compress. By calculating the residual between the quantized ground-truth CS measurements and their corresponding quantized inference measurements and using Huffman coding to associate each residual quantization index with a binary code, we can reduce the redundancies among CS measurements efficiently. Besides, the prediction and quantization process is designed to be layer-independent, which can save much of the encoding time. The proposed approach introduces a novel framework for using CS in the compression domain. The experimental results show that the proposed scheme can significantly outperform JPEG2000 and approach or reach the performance of HEVC-Intra on some test images.