A PCA-FVQ and 3D-SPECK combined hierarchical coding method for spectral imagery

In this paper, a hierarchical spectral imagery coding method has been proposed. The spectral imagery is encoded into two layers, i.e. base layer and enhancement layer. Firstly, an PCA-FVQ (Principal Component Analysis based Fast Vector Quantization) coding method has been proposed to generate the ba...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2012 IEEE 11th International Conference on Signal Processing Jg. 2; S. 1038 - 1042
Hauptverfasser: Yue, Yunfeng, Zhuo, Li, Wang, Suyu, Zhao, Yingdi, Shi, Chen
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2012
Schlagworte:
3D-SPECK
Binary sequences
Correlation
Encoding
Image coding
Image quality
Image reconstruction
Indexes
Principal component analysis
Signal to noise ratio
spectral imagery
Vector quantization
ISBN:9781467321969, 1467321966
ISSN:2164-5221
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, a hierarchical spectral imagery coding method has been proposed. The spectral imagery is encoded into two layers, i.e. base layer and enhancement layer. Firstly, an PCA-FVQ (Principal Component Analysis based Fast Vector Quantization) coding method has been proposed to generate the base layer bitstream, which can be decoded independently to provide a basic image quality. Then the differential data between the original image and the reconstructed image decoded from the base layer bitstream is encoded by 3D-SPECK algorithm to generate the enhancement layer bitstream which can provide an enhanced image quality. The experimental results show that, under the same coding conditions, compared with 3D-SPECK algorithm, the proposed method has achieved 1-15dB higher image quality improvement in terms of SNR (Signal to Noise) while the compression ratio can be controlled precisely.
ISBN:9781467321969
1467321966
ISSN:2164-5221
DOI:10.1109/ICoSP.2012.6491756