30 January 2003 Near-lossless image compression by adaptive prediction: new developments and comparison of algorithms
Author Affiliations +
Abstract
This paper describes state-of-the-art approaches to near-lossless image compression by adaptive causal DPCM and presents two advanced schemes based on crisp and fuzzy switching of predictors, respectively. The former relies on a linear-regression prediction in which a different predictor is employed for each image block. Such block-representative predictors are calculated from the original data set through an iterative relaxation-labeling procedure. Coding time are affordable thanks to fast convergence of training. Decoding is always performed in real time. The latter is still based on adaptive MMSE prediction in which a different predictor at each pixel position is achieved by blending a number of prototype predictors through adaptive weights calculated from the past decoded samples. Quantization error feedback loops are introduced into the basic lossless encoders to enable user-defined upper-bounded reconstruction errors. Both schemes exploit context modeling of prediction errors followed by arithmetic coding to enhance entropy coding performances. A thorough performance comparison on a wide test image set show the superiority of the proposed schemes over both up-to-date encoders in the literature and new/upcoming standards.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bruno Aiazzi, Bruno Aiazzi, Luciano Alparone, Luciano Alparone, Stefano Baronti, Stefano Baronti, "Near-lossless image compression by adaptive prediction: new developments and comparison of algorithms", Proc. SPIE 4793, Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications, (30 January 2003); doi: 10.1117/12.453512; https://doi.org/10.1117/12.453512
PROCEEDINGS
12 PAGES


SHARE
Back to Top