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Predictive partitioned vector quantization for hyperspectral sounder data compression
- Source :
- SPIE Proceedings.
- Publication Year :
- 2004
- Publisher :
- SPIE, 2004.
-
Abstract
- The compression of three-dimensional hyperspectral sounder data is a challenging task given its unprecedented size and nature. Vector quantization (VQ) is explored for the compression of this hyperspectral sounder data. The high dimensional vectors are partitioned into subvectors to reduce codebook search and storage complexity in coding of the data. The partitions are made by use of statistical properties of the sounder data in the spectral dimension. Moreover, the data is decorrelated at first to make it better suited for vector quantization. Due to the data characteristics, the iterative codebook generation procedure converges much faster and also leads to a better reconstruction of the sounder data. For lossless compression of the hyperspectral sounder data, the residual error and the quantization indices are entropy coded. The independent vector quantizers for different partitions make this scheme practical for compression of the large volume 3D hyperspectral sounder data.
- Subjects :
- Lossless compression
Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Codebook
Vector quantization
Hyperspectral imaging
Pattern recognition
Data compression ratio
Data_CODINGANDINFORMATIONTHEORY
Lossy compression
Entropy (information theory)
Artificial intelligence
business
Physics::Atmospheric and Oceanic Physics
Data compression
Subjects
Details
- ISSN :
- 0277786X
- Database :
- OpenAIRE
- Journal :
- SPIE Proceedings
- Accession number :
- edsair.doi...........28f0a8e25cd1962d0d69227f9f109752