Iterated function systems as neural networks
Neural Networks
Fractal image compression: theory and application
Fractal image compression: theory and application
Fast fractal image block coding based on local variances
IEEE Transactions on Image Processing
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Although the baseline fractal image encoding algorithm could obtain very high compression ratio in contrast with other compression methods, it needs a great deal of encoding time, which limits it to widely practical applications. In recent years, an accelerating algorithm based on variance is addressed and has shortened the encoding time greatly; however, in the meantime, the image fidelity is obviously diminished. In this paper, a neural network is utilized to modify the variance-based encoding algorithm, which makes the quality of reconstructed images improved remarkably as the encoding time is significantly reduced. Experimental results show that the reconstructed images quality measured by peak-signal-to-noise-ratio is better than conventional variance-based algorithm, while the time consumption for encoding and the compression ratio are almost the same as the conventional variance-based algorithm.