eprintid: 2549 rev_number: 6 eprint_status: archive userid: 6 dir: disk0/00/00/25/49 datestamp: 2015-02-02 09:53:13 lastmod: 2015-02-02 09:53:13 status_changed: 2015-02-02 09:53:13 type: book_section metadata_visibility: show creators_name: Minervini, Massimo creators_name: Rusu, Cristian creators_name: Tsaftaris, Sotirios A. creators_id: massimo.minervini@imtlucca.it creators_id: creators_id: sotirios.tsaftaris@imtlucca.it title: Unsupervised and supervised approaches to color space transformation for image coding ispublished: pub subjects: QA75 divisions: CSA full_text_status: none keywords: Image compression; JPEG 2000; color space transformation; statistical learning 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-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. date: 2014-10 date_type: published publisher: IEEE pagerange: 5576-5580 event_title: Image Processing (ICIP), 2014 IEEE International Conference on id_number: 10.1109/ICIP.2014.7026128 refereed: TRUE book_title: Proceedings of the IEEE International Conference on Image Processing (ICIP) official_url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7026128&isnumber=7024995 citation: 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)