Fast fractal image encoder using NN-based block classifier and improved isometry transformation
Hwanik Chung, Boohyung Lee, Hernsoo Hahn · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
This paper proposes a new fractal image encoder using a SOFM neural network based classifier and also an improved isometric transformation, to reduce the encoding time. Here the sizes of a domain block and range block are 8x8 pixels and 4x4 pixels, respectively. Block is classified into one of four patterns, based on the variation of intensities of the pixels in the block: flat where it is very low, middle where it is small, vertical/horizontal where there exists a vertical or horizontal edge, diagonal where there exists a diagonal edge. The SOFM neural network memorizes these patterns by competitive learning where the weights on the connections are determined by the Kohenen's learning rules. To reduce the searching time, the proposed algorithm searches domain blocks following a spiral trajectory starting from the block selected in the range and uses an improved isometric transformation which classifies the templates before comparison. The experimental results have shown that the proposed algorithm reduces the encoding speed by 50% on average while maintaining the same PSNR and bit rate, compared to the other's recent research results.