Fast fractal image encoding based on the extreme difference feature of normalized block

Feature vector method for fast fractal image encoding is considered as one of the most innovative and promising approaches, but it suffers from several drawbacks, especially high dimensionality of feature vectors. Thus, an alternative feature method to reduce fractal encoding time is proposed in thi...

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Vydáno v:2009 IEEE International Conference on Intelligent Computing and Intelligent Systems Ročník 4; s. 159 - 163
Hlavní autor: Gaoping Li
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2009
Témata:
Educational institutions
Encoding
fractal
Fractals
Image coding
Image quality
Image reconstruction
Joining processes
Runtime
Search methods
Sorting
the extreme difference feature
ISBN:9781424447541, 1424447542
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Shrnutí:Feature vector method for fast fractal image encoding is considered as one of the most innovative and promising approaches, but it suffers from several drawbacks, especially high dimensionality of feature vectors. Thus, an alternative feature method to reduce fractal encoding time is proposed in this paper. Which is based on a newly-defined concept of the extreme difference feature, and proved an inequality linking the root-mean-square and the extreme difference feature of normalized block mathematically. By sorting the blocks in the codebook according to their the extreme difference feature, the encoder uses the bisection search method to find out the nearest codebook block to an input range block in the sense of the extreme difference feature. After that, the encoder further visits the codebook blocks in the vicinity of the nearest codebook block in order to search out the best matched codebook block to the range block being encoded. Computer simulation on four popular 512×512 test images demonstrate that the proposed scheme not only can averagely achieve the speed up of 25 times, but also provides the same image quality as the full search algorithm. Besides, its performance is better than those of the variance-based algorithm and one-norm of normalised block algorithm.
ISBN:9781424447541
1424447542
DOI:10.1109/ICICISYS.2009.5357696