@incollection{eprints2549,
           month = {October},
       publisher = {IEEE},
           pages = {5576--5580},
          author = {Massimo Minervini and Cristian Rusu and Sotirios A. Tsaftaris},
           title = {Unsupervised and supervised approaches to color space transformation for image coding},
       booktitle = {Proceedings of the IEEE International Conference on Image Processing (ICIP)},
            year = {2014},
        abstract = {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-Lo{\`e}ve (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.},
             url = {http://eprints.imtlucca.it/2549/},
        keywords = {Image compression; JPEG 2000; color space transformation;   statistical learning
}
}