ICIP 2006, Atlanta, GA

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Paper Detail

Session:Lossless Image Coding
Time:Wednesday, October 11, 12:20 - 12:40
Presentation: Lecture
Topic: Image & Video Coding: Lossless image coding
Authors: Charles Boncelet; University of Delaware 
Abstract: We present a new lossless image compression algorithm called BCTW, for bitplane context tree weighting, and a corresponding study into lossless image compression using several number representations and various algorithms. BCTW processes the image bitplane by bitplane and uses Context Tree Weighting (CTW) to estimate the probability of each pixel bit being a 1 or 0. The bit is then compressed with an entropy coder, most likely an arithmetic coder. BCTW is a very good lossless image compressor. In the study, we also look at several number representations (twos-complement, Gray coding, and sign-magnitude) and compare BCTW to MedBzip2 and JPEG-LS.On the Waterloo Bragzone GreySet2 dataset, BCTW outperforms JPEG-LS by 13% and MedBzip2 by 5%. We find that representing the pixels in a Gray code does not help. Sign-magnitude helps sometimes, but not in BCTW. The MED transform helps bzip2, but not BCTW.