Fractals everywhere
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Fractal image compression: theory and application
Fractal image compression: theory and application
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Detecting and restoring the tampered images based on iteration-free fractal compression
Journal of Systems and Software
Genetic subgradient method for solving location-allocation problems
Applied Soft Computing
Schema genetic algorithm for fractal image compression
Engineering Applications of Artificial Intelligence
A GA-based UWB pulse waveform design method
Digital Signal Processing
Feasibility of applying genetic algorithms in space-time block coded multiuser detection systems
Digital Signal Processing
A canonic-signed-digit coded genetic algorithm for designing finite impulse response digital filter
Digital Signal Processing
Fractal image compression based on Delaunay triangulation and vector quantization
IEEE Transactions on Image Processing
Technique for fractal image compression using genetic algorithm
IEEE Transactions on Image Processing
Image coding based on a fractal theory of iterated contractive image transformations
IEEE Transactions on Image Processing
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In this paper, a genetic algorithm with a hybrid select mechanism is proposed to speed up the fractal encoder. First, all of the image blocks including domain blocks and range blocks are classified into three classes: smooth; horizontal/vertical edge; and diagonal/sub-diagonal edge, according to their discrete cosine transformation (DCT) coefficients. Then, during the GA evolution, the population of every generation is separated into two clans: a superior clan and an inferior clan, according to whether the chromosome type is the same as that of the range block to be encoded or not. The hybrid select mechanism proposed by us is used to select appropriate parents from the two clans in order to reduce the number of MSE computations and maintain the retrieved image quality. Experimental results show that, since the number of MSE computations in the proposed GA method is about half of the traditional GA method, the encoding time for the proposed GA method is less than that of the traditional GA method. For retrieved image quality, the proposed GA method is almost the same as the traditional GA method or only has a little decay. Moreover, in comparison with the full search method, the encoding speed of the proposed GA method is some 130 times faster than that of the full search method, whereas the retrieved image quality is still relatively acceptable.