A compression-boosting transform for two-dimensional data

Authors:
Qiaofeng Yang;Stefano Lonardi;Avraham Melkman
Affiliations:
Dept. of Computer Science & Engineering, University of California, Riverside, CA;Dept. of Computer Science & Engineering, University of California, Riverside, CA;Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Venue:
AAIM'06 Proceedings of the Second international conference on Algorithmic Aspects in Information and Management
Year:
2006

Citing 9
Cited 1

Approximating clique and biclique problems

Journal of Algorithms
Engineering the compression of massive tables: an experimental approach

SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
On bipartite and multipartite clique problems

Journal of Algorithms
Improving table compression with combinatorial optimization

SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Compression of Sparse Matrices by Arithmetic Coding

DCC '98 Proceedings of the Conference on Data Compression
Using Column Dependency to Compress Tables

DCC '04 Proceedings of the Conference on Data Compression
Fully automatic cross-associations

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Compressing large boolean matrices using reordering techniques

VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Compression of sparse matrices by blocked Rice coding

IEEE Transactions on Information Theory

Hierarchical, Parameter-Free Community Discovery

ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II

Quantified Score

Hi-index	0.00

Visualization

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.