Combining fractal image compression and vector quantization
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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 512 x 512 Lena image, our best scheme achieved a peak-signal-to-noise ratio of 32.50 dB at 0.25 bpp.
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HAMZAOUI, Raouf, Dietmar SAUPE, 2000. Combining fractal image compression and vector quantization. In: IEEE Transactions on Image Processing. 2000, 9(2), pp. 197-208. ISSN 1057-7149. eISSN 1941-0042. Available under: doi: 10.1109/83.821730BibTex
@article{Hamzaoui2000Combi-42195, year={2000}, doi={10.1109/83.821730}, title={Combining fractal image compression and vector quantization}, number={2}, volume={9}, issn={1057-7149}, journal={IEEE Transactions on Image Processing}, pages={197--208}, author={Hamzaoui, Raouf and Saupe, Dietmar} }
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