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Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients

This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The d...

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Detalles Bibliográficos
Autores principales: Ulacha, Grzegorz, Łazoryszczak, Mirosław
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857394/
https://www.ncbi.nlm.nih.gov/pubmed/36673299
http://dx.doi.org/10.3390/e25010156
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author Ulacha, Grzegorz
Łazoryszczak, Mirosław
author_facet Ulacha, Grzegorz
Łazoryszczak, Mirosław
author_sort Ulacha, Grzegorz
collection PubMed
description This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The data modeling stage of the proposed codec was based on linear (calculated by the non-MMSE method) and non-linear (complemented by a context-dependent constant component removal block) predictions. Prediction error coding uses a two-stage compression: an adaptive Golomb code and a binary arithmetic code. The proposed solution results in 30% shorter decoding times and a lower bit average than competing solutions (by 7.9% relative to the popular JPEG-LS codec).
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spelling pubmed-98573942023-01-21 Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients Ulacha, Grzegorz Łazoryszczak, Mirosław Entropy (Basel) Article This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The data modeling stage of the proposed codec was based on linear (calculated by the non-MMSE method) and non-linear (complemented by a context-dependent constant component removal block) predictions. Prediction error coding uses a two-stage compression: an adaptive Golomb code and a binary arithmetic code. The proposed solution results in 30% shorter decoding times and a lower bit average than competing solutions (by 7.9% relative to the popular JPEG-LS codec). MDPI 2023-01-12 /pmc/articles/PMC9857394/ /pubmed/36673299 http://dx.doi.org/10.3390/e25010156 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ulacha, Grzegorz
Łazoryszczak, Mirosław
Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients
title Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients
title_full Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients
title_fullStr Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients
title_full_unstemmed Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients
title_short Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients
title_sort lossless image coding using non-mmse algorithms to calculate linear prediction coefficients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857394/
https://www.ncbi.nlm.nih.gov/pubmed/36673299
http://dx.doi.org/10.3390/e25010156
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AT łazoryszczakmirosław losslessimagecodingusingnonmmsealgorithmstocalculatelinearpredictioncoefficients