6533b861fe1ef96bd12c42c2

RESEARCH PRODUCT

Data Compression Using Wavelet and Local Cosine Transforms

Valery A. ZheludevPekka NeittaanmäkiAmir Averbuch

subject

Discrete wavelet transformComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONWavelet transformData_CODINGANDINFORMATIONTHEORYcomputer.file_formatWavelet packet decompositionSet partitioning in hierarchical treesWaveletJPEG 2000Discrete cosine transformAlgorithmcomputerData compression

description

The chapter describes an algorithm that compresses two-dimensional data arrays, which are piece-wise smooth in one direction and have oscillating events in the other direction. Seismic, hyper-spectral and fingerprints data, for example, have such a mixed structure. The transform part of the compression process is an algorithm that combines wavelet and local cosine transform (LCT). The quantization and the entropy coding parts of the compression are taken from the SPIHT codec. To efficiently apply the SPIHT codec to a mixed coefficients array, reordering of the LCT coefficients takes place. On the data arrays, which have the mixed structure, this algorithm outperforms other algorithms that are based on the 2D wavelet transforms combined with the SPIHT coding and on the JPEG 2000 compression standard. The algorithm retains fine oscillating events even at a low bitrate. Its compression capabilities are also demonstrated on multimedia images that have a fine texture. The wavelet part in the mixed transform of the presented algorithm utilizes the library of Butterworth wavelet transforms described in Chap. 12.

yearjournalcountryeditionlanguage
2015-08-28
https://doi.org/10.1007/978-3-319-22303-2_13