Progressive Lossy-to-Lossless Compression of DNA Microarray Images
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Miguel_Hernandez-Cabronero-IEE ...
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Final Accepted Manuscript
Author
Hernandez-Cabronero, MiguelBlanes, Ian
Pinho, Armando J.
Marcellin, Michael W.
Serra-Sagrista, Joan
Affiliation
Univ Arizona, Dept Elect & Comp EngnIssue Date
2016-05
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Progressive Lossy-to-Lossless Compression of DNA Microarray Images 2016, 23 (5):698 IEEE Signal Processing LettersJournal
IEEE Signal Processing LettersRights
© 2016 IEEE.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The analysis techniques applied to DNA microarray images are under active development. As new techniques become available, it will be useful to apply them to existing microarray images to obtain more accurate results. The compression of these images can be a useful tool to alleviate the costs associated to their storage and transmission. The recently proposed Relative Quantizer (RQ) coder provides the most competitive lossy compression ratios while introducing only acceptable changes in the images. However, images compressed with the RQ coder can only be reconstructed with a limited quality, determined before compression. In this work, a progressive lossy-to-lossless scheme is presented to solve this problem. First, the regular structure of the RQ intervals is exploited to define a lossy-to-lossless coding algorithm called the Progressive RQ (PRQ) coder. Second, an enhanced version that prioritizes a region of interest, called the PRQ-region of interest (ROI) coder, is described. Experiments indicate that the PRQ coder offers progressivity with lossless and lossy coding performance almost identical to the best techniques in the literature, none of which is progressive. In turn, the PRQ-ROI exhibits very similar lossless coding results with better rate-distortion performance than both the RQ and PRQ coders.ISSN
1070-99081558-2361
Version
Final accepted manuscriptae974a485f413a2113503eed53cd6c53
10.1109/LSP.2016.2547893