Multispectral Image Compression with Discrete Wavelet Transformed Improved SPIHT using various Wavelets

2018 ◽  
Vol 6 (6) ◽  
pp. 100-106
Author(s):  
Bhagya Raju V ◽  
Dr. K. Jaya Sankar ◽  
. . ◽  
Dr. C. D. Naidu
Keyword(s):  
Image Compression ◽  
Multispectral Image ◽  
Discrete Wavelet ◽  
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

scholarly journals Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

2019 ◽  
Vol 11 (7) ◽  
pp. 759 ◽  
Author(s):  
Jin Li ◽  
Zilong Liu
Keyword(s):  
Neural Networks ◽  
Image Compression ◽  
Low Complexity ◽  
Multispectral Image ◽  
Small Scale ◽  
Three Dimension ◽  
Convolution Neural Networks ◽  
Computation Efficiency ◽  
Spectral Transform ◽  
Multispectral Image Compression

A multispectral image is a three-order tensor since it is a three-dimensional matrix, i.e.one spectral dimension and two spatial position dimensions. Multispectral image compression canbe achieved by means of the advantages of tensor decomposition (TD), such as NonnegativeTucker Decomposition (NTD). Unfortunately, the TD suffers from high calculation complexity andcannot be used in the on-board low-complexity case (e.g., multispectral cameras) that the hardwareresources and power are limited. Here, we propose a low-complexity compression approach formultispectral images based on convolution neural networks (CNNs) with NTD. We construct anew spectral transform using CNNs, where the CNNs are able to transform the three-dimensionspectral tensor from large-scale to a small-scale version. The NTD resources only allocate thesmall-scale three-dimension tensor to improve calculation efficiency. We obtain the optimizedsmall-scale spectral tensor by the minimization of original and reconstructed three-dimensionspectral tensor in self-learning CNNs. Then, the NTD is applied to the optimized three-dimensionspectral tensor in the DCT domain to obtain the high compression performance. We experimentallyconfirmed the proposed method on multispectral images. Compared to the case that the newspectral tensor transform with CNNs is not applied to the original three-dimension spectral tensorat the same compression bit-rates, the reconstructed image quality could be improved. Comparedwith the full NTD-based method, the computation efficiency was obviously improved with only asmall sacrifices of PSNR without affecting the quality of images.

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