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Hierarchical Coding of Binary Images
March 1985 (vol. 7 no. 3)
pp. 284-298
Yoav Cohen, National Institute for Testing and Evaluation, Jerusalem, Israel.
Michael S. Landy, Human Information Processing Laboratory, Department of Psychology, New York University, New York, NY 10003.
M. Pavel, Department of Psychology, Stanford University, Stanford, CA 94305.
Quadtrees are a compact hierarchical method of representation of images. In this paper, we explore a number of hierarchical image representations as applied to binary images, of which quadtrees are a single exemplar. We discuss quadtrees, binary trees, and an adaptive hierarchical method. Extending these methods into the third dimension of time results in several other methods. All of these methods are discussed in terms of time complexity, worst case and average compression of random images, and compression results on binary images derived from natural scenes. The results indicate that quadtrees are the most effective for two-dimensional images, but the adaptive algorithms are more effective for dynamic image sequences.
Citation:
Yoav Cohen, Michael S. Landy, M. Pavel, "Hierarchical Coding of Binary Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, no. 3, pp. 284-298, March 1985, doi:10.1109/TPAMI.1985.4767657
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