caa a22 4500 605468745 CHVBK 20210128100318.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00500-014-1436-0 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1436-0 El-said Shaimaa Electronics and Communications Department, Faculty of Engineering, Zagazig University, P.O. Box 44519, Zagazig, Egypt aut Image quantization using improved artificial fish swarm algorithm [Elektronische Daten] [Shaimaa El-said] Most image compression algorithms suffer from several drawbacks: high-computational complexity, moderate reconstructed picture qualities, and a variable bit rate. In this paper, an efficient color image quantization technique that depends on an optimized Fuzzy C-means (OFCM) algorithm is proposed. It exploits the optimization capability of the improved artificial fish swarm algorithm to overcome the shortage of Fuzzy C-means algorithm. It uses error diffusion algorithms to obtain perceptually better images after quantization. Experiments are carried out to estimate the performance of the proposed OFCM algorithm in image compression using standard image set. The results indicate that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction. The performance efficiency of the proposed technique is compared with those of three other quantization algorithms. The Comparative results confirmed that the OFCM has potential in terms of both accuracy and perceptual quality as compared to recent methods of the literature. Springer-Verlag Berlin Heidelberg, 2014 Image quantization nationallicence Compression nationallicence Data clustering nationallicence FCM nationallicence Swarm intelligence nationallicence Artificial fish swarm algorithm (AFSA) nationallicence Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2667-2679 1432-7643 19:9<2667 2015 19 500 https://doi.org/10.1007/s00500-014-1436-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1436-0 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- El-said Shaimaa Electronics and Communications Department, Faculty of Engineering, Zagazig University, P.O. Box 44519, Zagazig, Egypt aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2667-2679 1432-7643 19:9<2667 2015 19 500