Abstract
We introduce a novel invertible transform for two- dimensional data which has the objective of reordering the matrix so it will improve its (lossless) compression at later stages. The transform requires to solve a computationally hard problem for which a randomized algorithm is used. The inverse transform is fast and can be implemented in linear time in the size of the matrix. Preliminary experimental results show that the reordering improves the compressibility of digital images.
This project was supported in part by NSF CAREER IIS-0447773, and NSF DBI-0321756. AM was supported in part by the Paul Ivanier Center for Robotics Research and Production Management. A one-page abstract about this work appeared in the Proceedings of Data Compression Conference, Snowbird, Utah, 2005.
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Yang, Q., Lonardi, S., Melkman, A. (2006). A Compression-Boosting Transform for Two-Dimensional Data. In: Cheng, SW., Poon, C.K. (eds) Algorithmic Aspects in Information and Management. AAIM 2006. Lecture Notes in Computer Science, vol 4041. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775096_13
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DOI: https://doi.org/10.1007/11775096_13
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