Statistics – Applications
Scientific paper
Oct 1997
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3164..555a&link_type=abstract
Proc. SPIE Vol. 3164, p. 555-564, Applications of Digital Image Processing XX, Andrew G. Tescher; Ed.
Statistics
Applications
Scientific paper
Recent industry efforts have focused on the Balanced Tree Clustering (BTC) approach to speed up the encoding process for fractal image compression. Since the BTC algorithm compares intensity pixel by pixel for blocks in a mother cluster, clocks are divided into different clusters when they have different orientations or when they have different amplitudes of intensity offset with the averaged values but otherwise the same intensity distribution, causing poor clustering. In this paper we present and discuss the improvement of the original BTC algorithm by devising a method to include all 8 isometric versions of a block without enlarging the size of the domain pool and without sacrificing the encoding time. The results of compression are compared using the BTC algorithm based on the three different pixel values, intensity, intensity offset by the average value, and intensity variance, with and without considering 8 isometries of each block. For a sample image of 256 X 256 X 8 with 4 X 4 block size and with about 10 blocks in each cluster, the BTC using intensity variance and considering the 8 isometries the encoding speed about 200 times while decreases PSNR less than 8 percent over the ordered quadrant intensity classification into three classes. The BTC based on intensity variance also improves the encoding time and the fidelity of compression over the BTC based on the other two pixel values. The BTC algorithm based on the three different pixel values also generate different fractal code books which may affect the utility of the code books for image classification and image matching.
An Chang-Hyuk
Berry Kelly
Cosby Alan
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