scholarly journals Multispectral image compression for spectral and color reproduction based on lossy to lossless coding

2010 ◽  
Author(s):  
Kazuma Shinoda ◽  
Yuri Murakami ◽  
Masahiro Yamaguchi ◽  
Nagaaki Ohyama
Keyword(s):  
Image Compression ◽  
Multispectral Image ◽  
Color Reproduction ◽  
Lossless Coding ◽  
Multispectral Image Compression

Multispectral image compression for high-quality color reproduction using JPEG2000

2004 ◽  
Author(s):  
Ryota Mase ◽  
Yohei Kawasaki ◽  
Yuri Murakami ◽  
Takashi Obi ◽  
Masahiro Yamaguchi ◽  
...  
Keyword(s):  
Image Compression ◽  
Multispectral Image ◽  
Color Reproduction ◽  
High Quality ◽  
Multispectral Image Compression

scholarly journals Spectral–Spatial Feature Partitioned Extraction Based on CNN for Multispectral Image Compression

2020 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Fanqiang Kong ◽  
Kedi Hu ◽  
Yunsong Li ◽  
Dan Li ◽  
Shunmin Zhao
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Multispectral Imaging ◽  
Rapid Development ◽  
Spectral Feature ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Spatial Feature ◽  
Spatial Features ◽  
Multispectral Image Compression

Recently, the rapid development of multispectral imaging technology has received great attention from many fields, which inevitably involves the image transmission and storage problem. To solve this issue, a novel end-to-end multispectral image compression method based on spectral–spatial feature partitioned extraction is proposed. The whole multispectral image compression framework is based on a convolutional neural network (CNN), whose innovation lies in the feature extraction module that is divided into two parallel parts, one is for spectral and the other is for spatial. Firstly, the spectral feature extraction module is used to extract spectral features independently, and the spatial feature extraction module is operated to obtain the separated spatial features. After feature extraction, the spectral and spatial features are fused element-by-element, followed by downsampling, which can reduce the size of the feature maps. Then, the data are converted to bit-stream through quantization and lossless entropy encoding. To make the data more compact, a rate-distortion optimizer is added to the network. The decoder is a relatively inverse process of the encoder. For comparison, the proposed method is tested along with JPEG2000, 3D-SPIHT and ResConv, another CNN-based algorithm on datasets from Landsat-8 and WorldView-3 satellites. The result shows the proposed algorithm outperforms other methods at the same bit rate.

Spectral Dictionary Learning Based Multispectral Image Compression

2019 ◽  
pp. 140-144
Author(s):  
Wei Liang ◽  
Yinghui Wang ◽  
Wen Hao ◽  
Xiuxiu Li ◽  
Xiuhong Yang ◽  
...  
Keyword(s):  
Image Compression ◽  
Dictionary Learning ◽  
Multispectral Image ◽  
Multispectral Image Compression
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