Fractal image compression: a randomized approach
Pattern Recognition Letters
A Simple, General Model for the Affine Self-similarity of Images
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Color image compression using fast VQ with DCT based block indexing method
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
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In fractal image compression, the code is an efficient binary representation of a contractive mapping whose unique fixed point approximates the original image. The mapping is typically composed of affine transformations, each approximating a block of the image by another block (called domain block) selected from the same image. The search for a suitable domain block is time-consuming. Moreover, the rate distortion performance of most fractal image coders is not satisfactory. We show how a few fixed vectors designed from a set of training images by a clustering algorithm accelerates the search for the domain blocks and improves both the rate-distortion performance and the decoding speed of a pure fractal coder, when they are used as a supplementary vector quantization codebook. We implemented two quadtree-based schemes: a fast top-down heuristic technique and one optimized with a Lagrange multiplier method. For the 8 bits per pixel (bpp) luminance part of the 512κ512 Lena image, our best scheme achieved a peak-signal-to-noise ratio of 32.50 dB at 0.25 bpp