Arithmetic coding for data compression
Communications of the ACM
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
Digital Pictures: Representation and Compression
Digital Pictures: Representation and Compression
LOCO-I: a low complexity, context-based, lossless image compression algorithm
DCC '96 Proceedings of the Conference on Data Compression
Classified adaptive prediction and entropy coding for lossless coding of images
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Extending TMW for Near Lossless Compression of Greyscale Images
DCC '98 Proceedings of the Conference on Data Compression
Fuzzy logic-based matching pursuits for lossless predictive coding of still images
IEEE Transactions on Fuzzy Systems
Applications of universal context modeling to lossless compression of gray-scale images
IEEE Transactions on Image Processing
An image multiresolution representation for lossless and lossy compression
IEEE Transactions on Image Processing
Lossless compression of continuous-tone images via context selection, quantization, and modeling
IEEE Transactions on Image Processing
Lossless image compression by quantization feedback in a content-driven enhanced Laplacian pyramid
IEEE Transactions on Image Processing
Near-lossless image compression: minimum-entropy, constrained-error DPCM
IEEE Transactions on Image Processing
L∞ constrained high-fidelity image compression via adaptive context modeling
IEEE Transactions on Image Processing
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS
IEEE Transactions on Image Processing
Edge-directed prediction for lossless compression of natural images
IEEE Transactions on Image Processing
Information-theoretic assessment of multi-dimensional signals
Signal Processing - Special issue: Information theoretic signal processing
Virtually lossless compression of astrophysical images
EURASIP Journal on Applied Signal Processing
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This paper describes a differential pulse code modulation scheme suitable for lossless and near-lossless compression of monochrome still images. The proposed method is based on a classified linear-regression prediction followed by context-based arithmetic coding of the outcome residuals. Images are partitioned into blocks, typically 8 × 8, and a minimum mean square error linear predictor is calculated for each block. Given a preset number of classes, a clustering algorithm produces an initial guess of as many predictors to be fed to an iterative labelling procedure that classifies pixel blocks simultaneously refining the associated predictors. The final set of predictors is encoded, together with the labels identifying the class, and hence the predictor, to which each block belongs. A thorough performance comparison, both lossless and near-lossless, with advanced methods from the literature and both current and upcoming standards highlights the advantages of the proposed approach. The method provides impressive performances, especially on medical images. Coding time are affordable thanks to fast convergence of training and easy balance between compression and computation by varying the system's parameters. Decoding is always real-time thanks to the absence of training.