6533b838fe1ef96bd12a3a78

RESEARCH PRODUCT

Space-Frequency Quantization using Directionlets

Vladan VelisavljevicMartin VetterliBaltasar Beferull-lozano

subject

Computational complexity theorybusiness.industryWavelet transformBasis functionIterative reconstructionSet partitioning in hierarchical treesComputer visionArtificial intelligencebusinessQuantization (image processing)AlgorithmData compressionImage compressionMathematics

description

In our previous work we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets. Here, we combine the directionlets with the space-frequency quantization (SFQ) image compression method, originally based on the standard two-dimensional (2-D) wavelet transform (WT). We show that our new compression method outperforms the standard SFQ as well as the state-of-the-art compression methods, like SPIHT and JPEG-2000, in terms of the quality of compressed images, especially in a low-rate compression regime. We also show that the order of computational complexity remains the same, as compared to the complexity of the standard SFQ algorithm.

https://doi.org/10.1109/icip.2007.4379271