Soft Compression for Lossless Image Coding
This work addresses the problem of reducing bandwidth and storage space for image transmission and storage, which is relevant for anyone dealing with large image datasets.
This paper introduces soft compression, a lossless image compression method that aims to eliminate coding and spatial redundancy by encoding images using codebook locations and shapes. It proposes a 'compressible indicator function' to evaluate performance, investigating its application across binary, grayscale, and multi-component images.
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.