Near-lossless image compression by relaxation-labelled prediction
Signal Processing - Image and Video Coding beyond Standards
Content Layer Progressive Coding of Digital Maps
DCC '00 Proceedings of the Conference on Data Compression
Perceptual Digital Watermarking for Image Authentication in Electronic Commerce
Electronic Commerce Research
Double random field models for remote sensing image segmentation
Pattern Recognition Letters
Lossless compression of map contours by context tree modeling of chain codes
Pattern Recognition
Pattern Recognition Letters
Virtually lossless compression of astrophysical images
EURASIP Journal on Applied Signal Processing
Context-based entropy coding in AVS video coding standard
Image Communication
Mutual information-based context quantization
Image Communication
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
Lossless compression of map contours by context tree modeling of chain codes
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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Inspired by theoretical results on universal modeling, a general framework for sequential modeling of gray-scale images is proposed and applied to lossless compression. The model is based on stochastic complexity considerations and is implemented with a tree structure. It is efficiently estimated by a modification of the universal algorithm context. Several variants of the algorithm are described. The sequential, lossless compression schemes obtained when the context modeler is used with an arithmetic coder are tested with a representative set of gray-scale images. The compression ratios are compared with those obtained with state-of-the-art algorithms available in the literature, with the results of the comparison consistently favoring the proposed approach