A novel fractal image compression scheme with block classification and sorting based on Pearson's correlation coefficient.

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ID: 78711
2013
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Ranked #2 of 51 articles by views in ieee transactions on image processing : a publication of the ieee signal processing society

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Abstract
Fractal image compression (FIC) is an image coding technology based on the local similarity of image structure. It is widely used in many fields such as image retrieval, image denoising, image authentication, and encryption. FIC, however, suffers from the high computational complexity in encoding. Although many schemes are published to speed up encoding, they do not easily satisfy the encoding time or the reconstructed image quality requirements. In this paper, a new FIC scheme is proposed based on the fact that the affine similarity between two blocks in FIC is equivalent to the absolute value of Pearson's correlation coefficient (APCC) between them. First, all blocks in the range and domain pools are chosen and classified using an APCC-based block classification method to increase the matching probability. Second, by sorting the domain blocks with respect to APCCs between these domain blocks and a preset block in each class, the matching domain block for a range block can be searched in the selected domain set in which these APCCs are closer to APCC between the range block and the preset block. Experimental results show that the proposed scheme can significantly speed up the encoding process in FIC while preserving the reconstructed image quality well.
Reference Key
wang2013aieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Jianji;Zheng, Nanning;
Journal ieee transactions on image processing : a publication of the ieee signal processing society
Year 2013
DOI
10.1109/TIP.2013.2268977
URL
Keywords
β-tcp fractal geometry granular material lacunarity succolarity

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