Using predictive and differential methods with K2-Raster compact data structure for hyperspectral image lossless compression
This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structu...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:216643 |
| Acesso em linha: | https://ddd.uab.cat/record/216643 https://dx.doi.org/urn:doi:10.3390/rs11212461 |
| Access Level: | acceso abierto |
| Palavra-chave: | Compact data structure Quadtree K2-tree K2-raster DACs 3D-CALIC M-CALIC Hyperspectral images |
| Resumo: | This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k-raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k-raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding. |
|---|