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|Title:||Compressing as well as the best tiling of an image|
|Source:||Lee, W.S. (2000). Compressing as well as the best tiling of an image. IEEE International Symposium on Information Theory - Proceedings : 41-. ScholarBank@NUS Repository.|
|Abstract:||We investigate the task of compressing an image by using different probability models for compressing different regions of the image. We introduce a class of probability models for images, the k-rectangular tilings of an image, that 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 of N probability models. For an image of size n × n, we give a sequential probability assignment algorithm that codes the image with a code length which is within O(k log Nn/k) of the code length produced by the best probability model in the class. The algorithm has a computational complexity of O(Nn3). An interesting subclass of the class of k-rectangular tilings is the class of tilings using rectangles whose widths are powers of two. This class is far more flexible than quadtrees and yet has a sequential probability assignment algorithm that produces a code length that is within O(k log Nn/k) of the best model in the class with a computational complexity of O(Nn2 log n) (similar to the computational complexity of sequential probability assignment using quadtrees).|
|Source Title:||IEEE International Symposium on Information Theory - Proceedings|
|Appears in Collections:||Staff Publications|
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