Using simulated annealing to design good codes
IEEE Transactions on Information Theory
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Tree-structured vector quantization with significance map for wavelet image coding
DCC '95 Proceedings of the Conference on Data Compression
A greedy tree growing algorithm for the design of variable ratevector quantizers [image compression]
IEEE Transactions on Signal Processing
A vector quantization approach to universal noiseless coding and quantization
IEEE Transactions on Information Theory
Optimal pruning with applications to tree-structured source coding and modeling
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A fast LBG codebook training algorithm for vector quantization
IEEE Transactions on Consumer Electronics
Neural networks for vector quantization of speech and images
IEEE Journal on Selected Areas in Communications
Fast full search equivalent encoding algorithms for image compression using vector quantization
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
International Journal of Artificial Intelligence and Soft Computing
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This paper presents an improvement method for enhancing the encoding time complexity of the dynamic path tree structured vector quantization (DPTSVQ) based on the same image quality. We call it the genetic-based adaptive threshold selection method (GATSM). DPTSVQ has successfully solved the disadvantage of the multi-path TSVQ. DPTSVQ uses a critical function and a fixed threshold to judge whether the number of search paths can be increased. However, in some cases, the fixed threshold scheme also brings the problem of increasing the encoding time. We thus propose GATSM to solve this problem by using a set of images to train the thresholds for adapting their real practical need. Our experimental results show that the encoding time complexity of GATSM is superior to DPTSVQ based on the same image quality. In addition, we compare the image quality of GATSM with the encoding algorithm with fast comparison (EAWFC) based on the same encoding time. Comparison results show that GATSM provides better image quality than that of EAWFC.