Robust search method for fractal image encoding using fringe based property

In this paper, the fringe property based fractal image encoding is introduced to reduce the encoding time. The main drawback of fractal encoding is high encoding time. There have been various researches carried out to overcome the limitation of fractal encoding and to speed up the encoder. A remarkable improvement was made by neighborhood region search strategies which classify the image blocks on the basis of some feature vectors of image to restrict the region for best matching pair of domain and range and also reduces the search complexity to logarithmic time. The neighborhood search strategy used in spatial domain reduces the encoding time from linear time to logarithmic time. The same principle was followed by neighborhood search in DCT based lowest frequency domain by reducing the search space to gather image blocks with similar fringe shapes. In addition to spatial domain based approach, it take less time to map the range blocks and domain blocks which consecutively consumes lesser time to encode image. The proposed algorithm used k-nearest neighbor search to find the similar fringe shaped range and domain blocks to be mapped on the basis of DCT lowest coefficients in horizontal and vertical directions and then using similarity measure; the virtual codebook was prepared to encode the image and then decoded the image using averaging of pixels of selected domains. The proposed approach gave high PSNR value, high compression ratio and reduced MSE computations with little decay in image quality which is acceptable.