article · 1996
Karhunen-Loeve Based Iterated Function System Encodings
Cited by 2Abstract and details only
Abstract
: Iterated Function Systems (IFS) raster compression techniques achieve their results by identifying self-similarities in the source image. However, not all source images contain exploitable self-similarity. We describe a compression technique that combines a Karhunen-Loeve basis set (for non self-similar aspects of an image) with a block based IFS. 1. INTRODUCTION Iterated Function Systems (IFS), popularized in Barnsley [1], provide a concise way of coding certain classes of interscale (fractal or self-similar) redundancies. The Block IFS technique of Jacquin [2,3] was the first (published) automated solution to determining an IFS code for any given image. Jacquin's technique partitions an image into (large) domain blocks and (small) range blocks, and then finds best fitting mappings from the domains to the ranges. Currently, most practical IFS encodings are descended from Jacquin's pioneering technique. These descendents all share mappings with the following schema: data from a do...
Cite this paper
Horowitz, F. G. (1996). Karhunen-Loeve Based Iterated Function System Encodings.
Horowitz, F. G. “Karhunen-Loeve Based Iterated Function System Encodings.” 1996.
Horowitz, F. G. 1996. “Karhunen-Loeve Based Iterated Function System Encodings.”
Horowitz, F.G. (1996) ‘Karhunen-Loeve Based Iterated Function System Encodings’.
@article{horowitz1996karhunen,
title = {{Karhunen-Loeve Based Iterated Function System Encodings}},
author = {Horowitz, F. G.},
year = {1996}
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