Efficient Compressed Sensing Based Image Coding by Using Gray Transformation

TL;DR

This paper introduces a novel CS-based image coding system utilizing gray transformation to preprocess images, which centralizes sample distribution and significantly reduces bit depth, thereby enhancing compression efficiency.

Contribution

The paper proposes a new CS-based image coding method using gray transformation to improve compression performance by reducing bit depth requirements.

Findings

Outperforms traditional CS-based coding without gray transformation

Reduces bit depth for CS samples significantly

Improves overall compression efficiency

Abstract

In recent years, compressed sensing (CS) based image coding has become a hot topic in image processing field. However, since the bit depth required for encoding each CS sample is too large, the compression performance of this paradigm is unattractive. To address this issue, a novel CS-based image coding system by using gray transformation is proposed. In the proposed system, we use a gray transformation to preprocess the original image firstly and then use CS to sample the transformed image. Since gray transformation makes the probability distribution of CS samples centralized, the bit depth required for encoding each CS sample is reduced significantly. Consequently, the proposed system can considerably improve the compression performance of CS-based image coding. Simulation results show that the proposed system outperforms the traditional one without using gray transformation in terms of compression performance.