TY  - CHAP
ID  - eprints2549
EP  - 5580
T2  - Proceedings of the IEEE International Conference on Image Processing (ICIP)
TI  - Unsupervised and supervised approaches to color space transformation for image coding
AV  - none
KW  - Image compression; JPEG 2000; color space transformation;   statistical learning

Y1  - 2014/10//
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7026128&isnumber=7024995
A1  - Minervini, Massimo
A1  - Rusu, Cristian
A1  - Tsaftaris, Sotirios A.
PB  - IEEE
N2  - 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.
SP  - 5576
ER  -