Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
IEEE Transactions on Signal Processing
Wavelet packet image coding using space-frequency quantization
IEEE Transactions on Image Processing
Optimal bit allocation and best-basis selection for wavelet packets and TSVQ
IEEE Transactions on Image Processing
Fast adaptive wavelet packet image compression
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
In image compression, the wavelet transformation is a state-of-the-art component. Recently, wavelet packet decomposition has received quite an interest. A popular approach for wavelet packet decomposition is the near-best-basis algorithm using nonadditive cost functions. In contrast to additive cost functions, the wavelet packet decomposition of the near-best-basis algorithm is only suboptimal. We apply methods from the field of evolutionary computation (EC) to test the quality of the near-best-basis results. We observe a phenomenon: the results of the near-best-basis algorithm are inferior in terms of cost-function optimization but are superior in terms of rate/distortion performance compared to EC methods.