Fractals and chaos
Fractal image compression: theory and application
Fractal image compression: theory and application
Schema genetic algorithm for fractal image compression
Engineering Applications of Artificial Intelligence
Technique for fractal image compression using genetic algorithm
IEEE Transactions on Image Processing
A fast encoding algorithm for fractal image compression using the DCT inner product
IEEE Transactions on Image Processing
Speed-up in fractal image coding: comparison of methods
IEEE Transactions on Image Processing
Image coding based on a fractal theory of iterated contractive image transformations
IEEE Transactions on Image Processing
Study on huber fractal image compression
IEEE Transactions on Image Processing
Novel fractal image encoding algorithm using normalized one-norm and kick-out condition
Image and Vision Computing
Real time fractal image coder based on characteristic vector matching
Image and Vision Computing
Robust estimation of parameter for fractal inverse problem
Computers & Mathematics with Applications
Multispectral and multiresolution image fusion using particle swarm optimization
Multimedia Tools and Applications
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Fractal image compression is promising both theoretically and practically. The encoding speed of the traditional full search method is a key factor rendering the fractal image compression unsuitable for real-time applications. In this paper, particle swarm optimization (PSO) method by utilizing the visual information of the edge property is proposed, which can speedup the encoder and preserve the image quality. Instead of the full search, a direction map is built according to the edge-type of image blocks, which directs the particles in the swarm to regions consisting of candidates of higher similarity. Therefore, the searching space is reduced and the speedup can be achieved. Also, since the strategy is performed according to the edge property, better visual effect can be preserved. Experimental results show that the visual-based particle swarm optimization speeds up the encoder 125 times faster with only 0.89dB decay of image quality in comparison to the full search method.