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Unsupervised and supervised approaches to color space transformation for image coding

Minervini, Massimo, Rusu, Cristian and Tsaftaris, Sotirios A. (2014) Unsupervised and supervised approaches to color space transformation for image coding. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE, Paris, pp. 5576-5580. ISBN 9781479957514

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The linear transformation of input (typically RGB) data into a color space is important in image compression. Most schemes adopt fixed transforms to decorrelate the color channels. Energy compaction transforms such as the Karhunen-Loève (KLT) do entail a complexity increase. Here, we propose a new data-dependent transform (aKLT), that achieves compression performance comparable to the KLT, at a fraction of the computational complexity. More important, we also consider an application-aware setting, in which a classifier analyzes reconstructed images at the receiver's end. In this context, KLT-based approaches may not be optimal and transforms that maximize post-compression classifier performance are more suited. Relaxing energy compactness constraints, we propose for the first time a transform which can be found offline optimizing the Fisher discrimination criterion in a supervised fashion. In lieu of channel decorrelation, we obtain spatial decorrelation using the same color transform as a rudimentary classifier to detect objects of interest in the input image without adding any computational cost. We achieve higher savings encoding these regions at a higher quality, when combined with region-of-interest capable encoders, such as JPEG 2000.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions: School of Computing > Staff Research and Publications
Depositing User: Caoimhe Ní Mhaicín
Date Deposited: 03 Jul 2018 09:24
Last Modified: 03 Jul 2018 09:24

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