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Title:Brezizgubno stiskanje rastrskih slik z uporabo genetskega algoritma : magistrsko delo
Authors:ID Klobučar, Tomaž (Author)
ID Jesenko, David (Mentor) More about this mentor... New window
ID Bizjak, Marko (Comentor)
Files:.pdf MAG_Klobucar_Tomaz_2025.pdf (7,00 MB)
MD5: 0FB17CC28108C18F897AF6F0D9623E23
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu je predstavljena uporaba genetskega algoritma za brezizgubno stiskanje rastrskih slik. Poudarek je na kombiniranju genetskega algoritma z različnimi tehnikami stiskanja podatkov, vključno z aritmetičnim kodiranjem, metodo RLE (angl. Run Length Encoding) in Huffmanovim kodiranjem. Podrobno je opisano teoretično ozadje genetskega algoritma in njegovih osnovnih postopkov, kot so selekcija, križanje in mutacija. Prav tako je predstavljena implementacija genetskega algoritma, kodirnika in dekodirnika. Opravljene so bile analize vhodnih parametrov kodeka, stiskanja splošnih in risanih slik, vpliva napovedi genetskega algoritma na stopnjo stiskanja, vpliva pretvorbe barvnega prostora na stopnjo stiskanja ter analiza časovne zahtevnosti. Rezultati so pokazali, da predlagan kodek doseže stopnjo stiskanja primerljivo z izbranimi formati, njegova učinkovitost stiskanja pa se izboljša z uporabo pretvorbe barvnega prostora.
Keywords:brezizgubno stiskanje slik, risane slike, genetski algoritem, Huffmanovo kodiranje, aritmetično kodiranje, RLE
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[T. Klobučar]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (X, 45 str.))
PID:20.500.12556/DKUM-91639 New window
UDC:004.627:004.932(043.2)
COBISS.SI-ID:226810371 New window
Publication date in DKUM:06.02.2025
Views:171
Downloads:53
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:22.01.2025

Secondary language

Language:English
Title:Lossless raster image compression using genetic algorithm
Abstract:The thesis explores the application of a genetic algorithm for lossless compression of raster images. It focuses on integrating the genetic algorithm with various data compression techniques, including arithmetic coding, RLE (Run Length Encoding), and Huffman coding. The theoretical foundations of the genetic algorithm are discussed in detail, covering key processes such as selection, crossover, and mutation. Additionally, the implementation of a genetic algorithm, encoder, and decoder is presented. Analyses were conducted on the codec's input parameters, the compression of general and cartoon images, the impact of the genetic algorithm's prediction on compression rates, the impact of color space conversion on compression rates, and the algorithm's time complexity. The results demonstrate that the proposed codec achieves a compression rate comparable to selected formats, with its efficiency further improving when color space conversion is applied.
Keywords:lossless image compression, cartoon images, genetic algorithm, Huffman coding, arithmetic coding, RLE


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