Classification of face images using local iterated function systems
Machine Vision and Applications
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We propose a novel fractal image compression technique, which does not require iteration at the decoder. The main problem relating to the conventional noniterative algorithm is that the smooth region cannot be coded efficiently, since the size of the range block is limited to be less than 8/spl times/8. We alleviate this problem by generating two codebooks from a planarly approximated image. In other words, the first codebook is generated by the smoothing operator for large and smooth range blocks and the second codebook is generated by the spatial contraction operator for small and active range blocks, respectively. The computer simulation results on the real images demonstrate that the proposed algorithm provides much better performance than most other fractal-based coders, in terms of the subjective quality as well as the objective quality (PSNR). Moreover, the proposed algorithm is very fast in decoding, since it does not require iteration at the decoder.