Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predictive Lossless Compression of Regions of Interest in Hyperspectral Images With No-Data Regions
This paper addresses the problem of efficient predictive lossless compression on the regions of interest (ROIs) in the hyperspectral images with no-data regions. We propose a two-stage prediction scheme, where a context-similarity-based weighted average prediction is followed by recursive least square filtering to decorrelate the hyperspectral images for compression. We then propose to apply separate Golomb-Rice codes for coding the prediction residuals of the full-context pixels and boundary pixels, respectively. To study the coding gains of this separate coding scheme, we introduce a mixture geometric model to represent the residuals associated with various combinations of the full-context pixels and boundary pixels. Both information-theoretic analysis and simulations on synthetic data confirm the advantage of the separate coding scheme over the conventional coding method based on a single underlying geometric distribution. We apply the aforementioned prediction and coding methods to four publicly available hyperspectral image data sets, attaining significant improvements over several other state-of-the-art methods, including the shape-adaptive JPEG 2000 method.
Predictive Lossless Compression of Regions of Interest in Hyperspectral Images With No-Data Regions
This paper addresses the problem of efficient predictive lossless compression on the regions of interest (ROIs) in the hyperspectral images with no-data regions. We propose a two-stage prediction scheme, where a context-similarity-based weighted average prediction is followed by recursive least square filtering to decorrelate the hyperspectral images for compression. We then propose to apply separate Golomb-Rice codes for coding the prediction residuals of the full-context pixels and boundary pixels, respectively. To study the coding gains of this separate coding scheme, we introduce a mixture geometric model to represent the residuals associated with various combinations of the full-context pixels and boundary pixels. Both information-theoretic analysis and simulations on synthetic data confirm the advantage of the separate coding scheme over the conventional coding method based on a single underlying geometric distribution. We apply the aforementioned prediction and coding methods to four publicly available hyperspectral image data sets, attaining significant improvements over several other state-of-the-art methods, including the shape-adaptive JPEG 2000 method.
Predictive Lossless Compression of Regions of Interest in Hyperspectral Images With No-Data Regions
Shen, Hongda (Autor:in) / Pan, W. David / Wu, Dongsheng
2017
Aufsatz (Zeitschrift)
Englisch
Lokalklassifikation TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
Predictive Lossless Compression of Regions of Interest in Hyperspectral Images With No-Data Regions
| Online Contents | 2016
Multiband Lossless Compression of Hyperspectral Images
| Online Contents | 2009
Lossless to Lossy Dual-Tree BEZW Compression for Hyperspectral Images
| Online Contents | 2014
LETTERS - Clustered DPCM for the Lossless Compression of Hyperspectral Images
| Online Contents | 2003
Lossless Hyperspectral-Image Compression Using Context-Based Conditional Average
| Online Contents | 2007