Progressive lossy-to-lossless compression of DNA microarray images

Miguel Hernandez-Cabronero, Ian Blanes, Armando J. Pinho, Michael W. Marcellin, Joan Serra-Sagrista

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish (US)
Article number7442802
Pages (from-to)698-702
Number of pages5
JournalIEEE Signal Processing Letters
Volume23
Issue number5
DOIs
StatePublished - May 2016

Keywords

  • DNA microarray images
  • Image compression
  • Quantization

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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