Lossless Image Compression Using Context-Dependent Linear Prediction Based on Mean Absolute Error Minimization

被引：0

作者：

Ulacha, Grzegorz ^{[1
]}

Lazoryszczak, Miroslaw ^{[1
]}

机构：

[1] West Pomeranian Univ Technol Szczecin, Fac Comp Sci & Informat Technol, ul Zolnierska 49, PL-71210 Szczecin, Poland

来源：

ENTROPY | 2024年 / 26卷 / 12期

关键词：

entropy coding; iterative reweighted least squares; context-dependent coding; deep learning; lossless image coding;

D O I：

10.3390/e26121115

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

This paper presents a method for lossless compression of images with fast decoding time and the option to select encoder parameters for individual image characteristics to increase compression efficiency. The data modeling stage was based on linear and nonlinear prediction, which was complemented by a simple block for removing the context-dependent constant component. The prediction was based on the Iterative Reweighted Least Squares (IRLS) method which allowed the minimization of mean absolute error. Two-stage compression was used to encode prediction errors: an adaptive Golomb and a binary arithmetic coding. High compression efficiency was achieved by using an author's context-switching algorithm, which allows several prediction models tailored to the individual characteristics of each image area. In addition, an analysis of the impact of individual encoder parameters on efficiency and encoding time was conducted, and the efficiency of the proposed solution was shown against competing solutions, showing a 9.1% improvement in the bit average of files for the entire test base compared to JPEG-LS.

引用

页数：23

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