Adaptive colour decorrelation for predictive image codecs

François Pasteau (1) , Clément Strauss (1) , Marie Babel (1) , Olivier Déforges (1) , Laurent Bédat (1)

1 IETR - Institut d'Electronique et de Télécommunications de Rennes

When considering color images and more generally multi component images, state of the art image codecs usually achieve component decorrelation through static color transforms such as YUV or YCoCg. This approach leads to suboptimal results as statistics of the image are not taken into account. The new approach proposed here offers to remove the correlation of one component according to another adaptively during the prediction process of an image codec. Through two jointly used processes, one aiming at choosing the best predictor of a component and another aiming at improving the predictor's effectiveness, this new approach improves both image quality and compression ratio. This new technique has been applied to the LAR codec and shows an improvement over previous studies up to 20% in rate and 0.5db in PSNR at low bit rates.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

François Pasteau, Clément Strauss, Marie Babel, Olivier Déforges, Laurent Bédat. Adaptive colour decorrelation for predictive image codecs. European Signal Processing Conference, EUSIPCO, Aug 2011, Barcelona, Spain. pp.1-5. ⟨hal-00600354⟩