Predicting Nearly As Well As the Best Pruning of a Decision Tree
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
DCC '97 Proceedings of the Conference on Data Compression
Efficient Context-Based Entropy Coding Lossy Wavelet Image Compression
DCC '97 Proceedings of the Conference on Data Compression
Switching Between Two Universal Source Coding Algorithms
DCC '98 Proceedings of the Conference on Data Compression
Coding for a binary independent piecewise-identically-distributed source
IEEE Transactions on Information Theory - Part 2
The context-tree weighting method: basic properties
IEEE Transactions on Information Theory
Comparison of different methods of classification in subband coding of images
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Computers and Electrical Engineering
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We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In an earlier paper, we introduced a class of probability models for images, the k-rectangular tiling of an image, which is formed by partitioning the image into k rectangular regions and generating the coefficients within each region by using a probability model selected from a finite class C of probability models.We also described a computationally efficient sequential probability assignment algorithm that is able to code an image with a code length that is close to the code length produced by the best model in the class. In this paper, we investigate the performance of the algorithm experimentally on the task of compressing wavelet subbands.We compare the method with compression methods that aim to compress as well as the best pruning of a quad-tree and compression methods that exploit the local statistics in a window around the coefficient being compressed. For a class C consisting of a small number of Laplacian distributions and the uniform distribution, we find that the best tiling method works best, but the difference in performance is significant for only a few of the images tested.