A case study on entropy-aware block-based linear transforms for lossless image compression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43974985" target="_blank" >RIV/49777513:23520/24:43974985 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s41598-024-79038-2" target="_blank" >https://www.nature.com/articles/s41598-024-79038-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-79038-2" target="_blank" >10.1038/s41598-024-79038-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A case study on entropy-aware block-based linear transforms for lossless image compression

Popis výsledku v původním jazyce

Data compression algorithms tend to reduce information entropy, which is crucial, especially in the case of images, as they are data intensive. In this regard, lossless image data compression is especially challenging. Many popular lossless compression methods incorporate predictions and various types of pixel transformations, in order to reduce the information entropy of an image. In this paper, a block optimisation programming framework is introduced to support various experiments on raster images, divided into blocks of pixels. Eleven methods were implemented, including prediction methods, string transformation methods, and inverse distance weighting, as a representative of interpolation methods.

Název v anglickém jazyce

A case study on entropy-aware block-based linear transforms for lossless image compression

Popis výsledku anglicky

Data compression algorithms tend to reduce information entropy, which is crucial, especially in the case of images, as they are data intensive. In this regard, lossless image data compression is especially challenging. Many popular lossless compression methods incorporate predictions and various types of pixel transformations, in order to reduce the information entropy of an image. In this paper, a block optimisation programming framework is introduced to support various experiments on raster images, divided into blocks of pixels. Eleven methods were implemented, including prediction methods, string transformation methods, and inverse distance weighting, as a representative of interpolation methods.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GF23-04622L" target="_blank" >GF23-04622L: Komprese dat založená na vynechání samozřejmé informace - COMPROMISE</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Svazek periodika

14

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

001367580400021

EID výsledku v databázi Scopus

2-s2.0-85210572405