Soft Compression for Lossless Image Coding

TL;DR

This paper introduces a novel lossless image compression method called soft compression, which reduces redundancy by encoding image locations and shapes, and proposes a compressible indicator to evaluate its performance.

Contribution

The paper presents a new concept of compressible indicator function and analyzes soft compression across different image types, enhancing understanding of its efficiency.

Findings

Reduces bandwidth and storage space for images.

Provides a new metric to evaluate compression performance.

Effective for binary, gray, and multi-component images.

Abstract

Soft compression is a lossless image compression method, which is committed to eliminating coding redundancy and spatial redundancy at the same time by adopting locations and shapes of codebook to encode an image from the perspective of information theory and statistical distribution. In this paper, we propose a new concept, compressible indicator function with regard to image, which gives a threshold about the average number of bits required to represent a location and can be used for revealing the performance of soft compression. We investigate and analyze soft compression for binary image, gray image and multi-component image by using specific algorithms and compressible indicator value. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images can be greatly reduced by applying soft compression.