Neural Networks
Multilevel Minimum Cross Entropy Threshold Selection Based on Quantum Particle Swarm Optimization
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
Honey Bee Mating Optimization Vector Quantization Scheme in Image Compression
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization
Expert Systems with Applications: An International Journal
Image compression method using improved PSO vector quantization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Balanced artificial bee colony algorithm
International Journal of Artificial Intelligence and Soft Computing
Hi-index | 12.05 |
The vector quantization (VQ) was a powerful technique in the applications of digital image compression. The traditionally widely used method such as the Linde-Buzo-Gray (LBG) algorithm always generated local optimal codebook. Recently, particle swarm optimization (PSO) is adapted to obtain the near-global optimal codebook of vector quantization. An alternative method, called the quantum particle swarm optimization (QPSO) had been developed to improve the results of original PSO algorithm. In this paper, we applied a new swarm algorithm, honey bee mating optimization, to construct the codebook of vector quantization. The results were compared with the other three methods that are LBG, PSO-LBG and QPSO-LBG algorithms. Experimental results showed that the proposed HBMO-LBG algorithm is more reliable and the reconstructed images get higher quality than those generated from the other three methods.