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Title:Uporaba evolucijskega računanja za zmanjšanje zaznave napak pri stresanju rastrskih slik : diplomsko delo
Authors:ID Vidovič, Blaž (Author)
ID Kohek, Štefan (Mentor) More about this mentor... New window
ID Podgorelec, David (Comentor)
Files:.pdf VS_Vidovic_Blaz_2025.pdf (3,60 MB)
MD5: 966B841C706DB52A1A02E6375C4F0CF6
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Diplomsko delo obravnava stresanje rastrskih slik in njegovo optimizacijo. Z namenom zmanjšanja zaznave napake smo matriko algoritma za stresanje Jarvis, Judice in Ninke prilagodili s pomočjo diferencialne evolucije. Skozi optimizacijo smo spreminjali uteži v matriki tako, da smo se poskušali s stresano sliko čim bolj približati originalni sliki. Za merjenje podobnosti med dvema slikama smo uporabili metriki PSNR in SSIM. Z večkratnim zagonom optimizacije smo analizirali vpliv različnih parametrov diferencialne evolucije na kakovost končnega rezultata. Ugotovili smo, da izboljšan SSIM ne pomeni nujno boljše ujemanje stresane slike z originalno sliko.
Keywords:rastrska slika, stresanje, Jarvis Judice in Ninke, optimizacija, evolucijski algoritmi, diferencialna evolucija
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[B. Vidovič]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (XIII, 68 str.))
PID:20.500.12556/DKUM-94512 New window
UDC:[004.021:575.82]:004.92(043.2)
COBISS.SI-ID:257421827 New window
Publication date in DKUM:23.09.2025
Views:186
Downloads:47
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:22.08.2025

Secondary language

Language:English
Title:Using evolutionary computation for image dithering to minimize perceptual error
Abstract:This diploma thesis deals with raster image dithering and its optimization. To minimize the perception error, we adapted the Jarvis, Judice and Ninka dithering algorithm matrix using differential evolution. Through optimization, we adjusted the weights in the matrix so that the dithered image was as similar as possible to the original image. To measure the similarity between the two images, we used the PSNR and SSIM metrics. By running the optimization multiple times, we analysed the impact of the differential evolution parameters on the quality of the result. We discovered that improved SSIM does not necessarily improve similarity between the dithered image and the original image.
Keywords:raster image, dither, Jarvis Judice and Ninke, optimization, evolutionary algorithms, differential evolution


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