Image quantization using improved artificial fish swarm algorithm
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605468745 | ||
| 003 | CHVBK | ||
| 005 | 20210128100318.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150901xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1436-0 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1436-0 | ||
| 100 | 1 | |a El-said |D Shaimaa |u Electronics and Communications Department, Faculty of Engineering, Zagazig University, P.O. Box 44519, Zagazig, Egypt |4 aut | |
| 245 | 1 | 0 | |a Image quantization using improved artificial fish swarm algorithm |h [Elektronische Daten] |c [Shaimaa El-said] |
| 520 | 3 | |a 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. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Image quantization |2 nationallicence | |
| 690 | 7 | |a Compression |2 nationallicence | |
| 690 | 7 | |a Data clustering |2 nationallicence | |
| 690 | 7 | |a FCM |2 nationallicence | |
| 690 | 7 | |a Swarm intelligence |2 nationallicence | |
| 690 | 7 | |a Artificial fish swarm algorithm (AFSA) |2 nationallicence | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/9(2015-09-01), 2667-2679 |x 1432-7643 |q 19:9<2667 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1436-0 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1436-0 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 100 |E 1- |a El-said |D Shaimaa |u Electronics and Communications Department, Faculty of Engineering, Zagazig University, P.O. Box 44519, Zagazig, Egypt |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/9(2015-09-01), 2667-2679 |x 1432-7643 |q 19:9<2667 |1 2015 |2 19 |o 500 | ||