Image Compression Based on Clustering Fuzzy Neural Network

Khaleel, Shahba I.; Majeed, Jamal S.; Khaleel, Bayda I.

doi:10.33899/csmj.2007.164023

	Image Compression Based on Clustering Fuzzy Neural Network
AL-Rafidain Journal of Computer Sciences and Mathematics
Article 12, Volume 4, Issue 2, 2007, Pages 157-174 PDF (557.72 K)
Document Type: Research Paper
DOI: 10.33899/csmj.2007.164023
Authors
Shahba I. Khaleel; Jamal S. Majeed; Bayda I. Khaleel
College of Computer Sciences and Mathematics University of Mosul, Iraq
Abstract
The problem inherent to any digital image is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop algorithm that compress images to lower data rates with better quality. This research present, a new approach to image compression based on clustering. This new approach includes new objective function, and its minimization by energy function based on unsupervised two dimensional fuzzy Hopfield neural network. New objective function consists of a combination of classification entropy function and average distance between image pixels and cluster centers. After applying new method on gray scale sample images at different number of clusters, better compression ratio and signal to noise ratio was observed. The new method is also a new clustering analysis method, and it provides more compact and separate clustering.
Keywords
Image Compression; clustering; unsupervised fuzzy Hopfield neural network; classification entropy function

Statistics Article View: 265 PDF Download: 244