Near-lossless image compression by relaxation-labelled prediction
Signal Processing - Image and Video Coding beyond Standards
Hybrid Lossless Coder of Medical Images with Statistical Data Modelling
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Lossless Image Data Sequence Compression Using Optimal Context Quantization
DCC '01 Proceedings of the Data Compression Conference
Compression of mammograms for medical practice
Proceedings of the 2004 ACM symposium on Applied computing
A multi-segment image coding and transmission scheme
Signal Processing
Context Based Error Modeling for Lossless Compression of EEG Signals Using Neural Networks
Journal of Medical Systems
EURASIP Journal on Applied Signal Processing
Lossless-by-Lossy Coding for Scalable Lossless Image Compression
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Context-based entropy coding in AVS video coding standard
Image Communication
Mutual information-based context quantization
Image Communication
Context clustering in lossless compression of gray-scale image
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
An evaluation of image compression algorithms for colour retinal images
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Lossless compression of CCD sensor data
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Medical image integrity control combining digital signature and lossless watermarking
DPM'09/SETOP'09 Proceedings of the 4th international workshop, and Second international conference on Data Privacy Management and Autonomous Spontaneous Security
Lossless image coding based on inter-color prediction for ultra high definition image
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
International Journal of Telemedicine and Applications
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Context modeling is an extensively studied paradigm for lossless compression of continuous-tone images. However, without careful algorithm design, high-order Markovian modeling of continuous-tone images is too expensive in both computational time and space to be practical. Furthermore, the exponential growth of the number of modeling states in the order of a Markov model can quickly lead to the problem of context dilution; that is, an image may not have enough samples for good estimates of conditional probabilities associated with the modeling states. New techniques for context modeling of DPCM errors are introduced that can exploit context-dependent DPCM error structures to the benefit of compression. New algorithmic techniques of forming and quantizing modeling contexts are also developed to alleviate the problem of context dilution and reduce both time and space complexities. By innovative formation, quantization, and use of modeling contexts, the proposed lossless image coder has a highly competitive compression performance and yet remains practical