title: Lossless compression of color mosaic images.
abstract: Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.
authors.affiliation: Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada. ningzhang@mail.ece.mcmaster.ca
authors.forename: Ning
authors.initials: N
authors.lastname: Zhang
authors.forename: Xiaolin
authors.initials: X
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003116:Color; D003124:Colorimetry / Q000379:methods*; D003195:Computer Communication Networks; D003196:Computer Graphics; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted