An Arithmetic Coding Scheme for Blocked-based Compressive Sensing of Images

TL;DR

This paper introduces an arithmetic coding scheme for the quantization indices in a DPCM-plus-SQ framework for block-based image compressive sensing, improving rate-distortion performance by reducing redundancy.

Contribution

It proposes a novel arithmetic coding method tailored for the DPCM-plus-SQ scheme in image compressive sensing, enhancing compression efficiency.

Findings

Achieves better rate-distortion performance than original DPCM-plus-SQ.

Outperforms transform coefficient coding in CABAC.

Reduces statistical redundancies in quantization indices.

Abstract

Differential pulse-code modulation (DPCM) is recently coupled with uniform scalar quantization (SQ) to improve the rate-distortion (RD) performance for the block-based quantized compressive sensing (CS) of images. In this framework, for each block's CS measurements, a prediction is generated based on the reconstructed CS measurements of the previous blocks and subtracted from measurements of the current block in the measurement domain. The resulting residual is then quantized by uniform SQ to generate the quantization index. However, the entropy coding is still required to remove the statistical redundancies between the quantization indices and generate the bitstream. Thus, in this paper, we proposed an arithmetic coding scheme for the quantization index within DPCM-plus-SQ framework by analyzing their statistics. Experimental results demonstrate that further RD performance can be achieved compared to original DPCM-plus-SQ scheme and transform coefficient coding in CABAC.