{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T13:41:00Z","timestamp":1785332460826,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/e26121115","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T06:44:33Z","timestamp":1734677073000},"page":"1115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Lossless Image Compression Using Context-Dependent Linear Prediction Based on Mean Absolute Error Minimization"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5726-6251","authenticated-orcid":false,"given":"Grzegorz","family":"Ulacha","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. \u017bo\u0142nierska 49, 71-210 Szczecin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7653-5982","authenticated-orcid":false,"given":"Miros\u0142aw","family":"\u0141azoryszczak","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. \u017bo\u0142nierska 49, 71-210 Szczecin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/TITB.2004.838376","article-title":"Motion compensated lossy-to-lossless compression of 4-D medical images using integer wavelet transforms","volume":"9","author":"Kassim","year":"2005","journal-title":"IEEE Trans. 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