| dc.contributor.author | Harman, F. and Koçyiǧit, Y. | |
| dc.date.accessioned | 2020-07-02T06:08:23Z | |
| dc.date.available | 2020-07-02T06:08:23Z | |
| dc.date.issued | 2018 | |
| dc.identifier.citation | cited By 1 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046270781&partnerID=40&md5=5eb75cacfd60e5bb0949b33ae4531a70 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12481/11624 | |
| dc.description.abstract | The importance of image compression problem has been progressing with the development of technology. The usage of genetic algorithm has become widespread in this field. In this study, the general structure of genetic algorithm and its effects on image compression are analyzed. In this study, it is seen that the creation of population via natural selection, the ratio of mutation and crossover affect the performance of image compression a lot. Roulette Wheel Selection and Elitist Selection that are the most known natural selections are firstly implemented on the standard image. But with these known natural selections, MSE (mean square error) and PSNR (peak signal noise ratio) are seen close to each other. It is seen that in all implementation with the 10% crossover and 5% mutation ratio, the natural selection algorithm based on pools has better MSE and PSNR values than genetic algorithm based on roulette wheel and elitist selection respectively. © 2017 EMO (Turkish Chamber of Electrical Enginners). | |
| dc.language.iso | English | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.title | A new approach to genetic algorithm in image compression | |
| dc.type | Conference Paper | |
| dc.contributor.department | Electrical and Electronics Engineering Dep., Manisa Celal Bayar University, Manisa, Turkey | |
| dc.identifier.volume | 2018-January | |
| dc.identifier.pages | 894-898 | |
