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

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...

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Vydané v:Entropy (Basel, Switzerland) Ročník 26; číslo 12; s. 1115
Hlavní autori: Ulacha, Grzegorz, Łazoryszczak, Mirosław
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 01.12.2024
MDPI
Predmet:
Algorithms
Analysis
Arithmetic coding
Binary codes
Context
context-dependent coding
Data compression
Decoding
deep learning
Efficiency
entropy coding
Image compression
Impact analysis
iterative reweighted least squares
Linear prediction
lossless image coding
Medical imaging equipment
Methods
Optimization
Parameters
Prediction models
Switching theory
Time dependence
Wavelet transforms
deep learning
context-dependent coding
lossless image coding
entropy coding
iterative reweighted least squares
ISSN:1099-4300, 1099-4300
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:1099-4300
1099-4300
DOI:10.3390/e26121115