Progressive lossy-to-lossless compression of DNA microarray images

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 associ...

Descripción completa

Detalles Bibliográficos
Autores: Hernández-Cabronero, Miguel|||0000-0001-9301-4337, Blanes Garcia, Ian|||0000-0001-8939-1666, Pinho, Armando J., Marcellin, Michael W.|||0000-0001-9606-134X, Serra-Sagristà, Joan|||0000-0003-4729-9292
Tipo de recurso: artículo
Fecha de publicación:2016
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:168571
Acceso en línea:https://ddd.uab.cat/record/168571
https://dx.doi.org/urn:doi:10.1109/LSP.2016.2547893
Access Level:acceso abierto
Palabra clave:DNA microarray images
Image compression
Quantization
Descripción
Sumario: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.