Improving Image Compression Through The Use
Of Evolutionary Computing Algorithms
Author(s)
O. L´opez-Granado, V. Galiano, A.Mart´ı, H. Migall´on,
M. Mart´ınez-Rach, P. Pi˜nol & M. P. Malumbres
Abstract
Discrete Wavelet Transform has proved to be powerful for image compression
because it is able to compact frequency and spatial localization of image energy
into a small fraction of coefficients. For a long time it was assumed that there is no
compression gain when coding the sign of wavelet coefficients. However, several
attempts were carried out and several image encoders like JPEG 2000 include sign
coding capabilities. In this paper, we analyze the convenience of including sign
coding techniques in tree-based wavelet image encoders, showing their benefits
(bit-rate saving). In order to exploit the scarce redundancy of wavelet coefficients
sign, we propose the use of machine learning approaches, like evolutionary
algorithms, to find the best sign prediction scheme that maximizes the resulting
compression rate.We have developed a sign predictionmodule based on the results
provided by the evolutionary algorithms, which it is able to work with whatever
the tree-based wavelet encoder like SPIHT, LTW, and others. After performing
several experiments, we have observed that, by including the proposed sign coding
capabilities, the sign compression gain is up to 17%. These results show that sign
coding techniques are of interest for improving compression rate, especially when
working with large images (2Mpixel and beyond) at low compression rates (high
quality).
Keywords
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