Research on cultural-based multi-objective particle swarm optimization in image compression quality assessment

Abstract Quantization is a main factor which affects the performance of the JPEG compression. Compression ratio and the quality of the decoded images are both determined by quantization tables. Appropriate choice of the quantization tables is the key to the Compression performance. To select different optimal quantization tables for different classes of images is multi-objective optimal problem. A cultural-based multi-objective particle swarm optimization model is proposed in this paper. And, different trade-offs between image compression and quality is presented. The simulation result shows that the proposed model is effective to the choice of image compression quality.

[1]  Robert G. Reynolds,et al.  Multi-objective Cultural Algorithms , 2010, IEEE Congress on Evolutionary Computation.

[2]  Gary G. Yen,et al.  Cultural MOPSO: A cultural framework to adapt parameters of multiobjective particle swarm optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[3]  Carlos A. Coello Coello,et al.  Evolutionary multiobjective optimization using a cultural algorithm , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[4]  Gary G. Yen,et al.  Cultural-Based Multiobjective Particle Swarm Optimization , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Yonggang Wu,et al.  Application of Multi-objective Cultural Algorithm in Water Resources Optimization , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[6]  Beatrice Lazzerini,et al.  A multi-objective evolutionary approach to image quality/compression trade-off in JPEG baseline algorithm , 2010, Appl. Soft Comput..