relation: http://eprints.imtlucca.it/2549/
title: Unsupervised and supervised approaches to color space transformation for image coding
creator: Minervini, Massimo
creator: Rusu, Cristian
creator: Tsaftaris, Sotirios A.
subject: QA75 Electronic computers. Computer science
description: 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.
publisher: IEEE
date: 2014-10
type: Book Section
type: PeerReviewed
identifier:   Minervini, Massimo and Rusu, Cristian and Tsaftaris, Sotirios A.  Unsupervised and supervised approaches to color space transformation for image coding.   In:  Proceedings of the IEEE International Conference on Image Processing (ICIP).   IEEE, pp. 5576-5580.       (2014)  
relation: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7026128&isnumber=7024995
relation: 10.1109/ICIP.2014.7026128