Abstract
Fractal image compression (FIC) is a relatively recent technique that represents an image by a contractive transform on the space of images, in which the fixed point is close to the image. Conventional fractal image compression method requires a large amount of encoding time in range-domain mapping. In this chapter, a novel noniterative fractal compression method is proposed to meet both the quality of output image and to speed up the encoding process of FIC. The proposed method works based on the fact that the absolute value of Pearson’s correlation coefficient (APCC) between two image blocks is equal to the affine similarity between them. Here, all the domain blocks are classified into three classes according to the APCC value. When matching block for a range block is searched, instead of searching the entire domain pool, only appropriate classified domain pool is used. Here, the mean image of the given image is taken as the domain pool. This proposed method can significantly speed up the encoding process as well as preserve the reconstructed image quality.
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Valarmathi, M.L., Sobia, M., Babyla Devi, R. (2015). Iteration-Free Fractal Image Compression Using Pearson’s Correlation Coefficient-Based Classification. In: Rajsingh, E., Bhojan, A., Peter, J. (eds) Informatics and Communication Technologies for Societal Development. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1916-3_16
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DOI: https://doi.org/10.1007/978-81-322-1916-3_16
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