A Compression-Boosting Transform for Two-Dimensional Data

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.

• Type: Conference
• Year: 2006
• Where: AAIM
• Authors: Qiaofeng Yang, Stefano Lonardi, Avraham Melkman
• Keywords: AAIM 2006, Algorithms, Inverse Transform, Randomized Algorithm, Two-dimensional Data