JPEG2000 Encoding of Remote Sensing Multispectral Images With No-Data Regions

González-Conejero, Jorge; Bartrina-Rapesta, Joan; Serra-Sagristà, Joan

doi:10.1109/lgrs.2009.2032370

Cited by 25 publications

(11 citation statements)

References 17 publications

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“…The proposed algorithm on the experimental multispectral images was compared with PCA, SPIHT+2D-DWT with KLT [45], SPECK+2D-DWT [46] with KLT, and POT [47]. The average PSNR was considered as the PSNR of the corresponding method.…”

Section: Resultsmentioning

confidence: 99%

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

Liu

2019

Remote Sensing

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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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Section: Resultsmentioning

confidence: 99%

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

Liu

2019

Remote Sensing

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“…For example, the no-data region 15 is very unimportant as the BG, thus this application has larger threshold values compared to the application where the BG is used for locating. For example, the no-data region 15 is very unimportant as the BG, thus this application has larger threshold values compared to the application where the BG is used for locating.…”

Section: Proposed Algorithm To Calculatementioning

confidence: 99%

Background pretruncated method for JPEG2000 region of interest coding

Wang¹,

Zhu²

2015

J. Electron. Imaging

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Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 09/29/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspxAbstract. We propose a new method for JPEG2000 region of interest (ROI) coding called the background pretruncated method (BPTM). An algorithm to calculate the key parameter in the BPTM is also proposed. The published JPEG2000 ROI coding methods have difficulty in balancing ROI quality and system computation. Methods based on shifting coefficients add much computation, and they significantly penalize the coding efficiency. Implicit methods cannot obtain as high-ROI quality as the coefficient shift methods. To efficiently achieve ROI coding, the BPTM assigns a proper number of the least significant bitplanes of the background to zero, and it can not only achieve high-ROI quality but also add no computation to the other modules in JPEG2000. Additionally, the BPTM supports arbitrary shaped ROI coding, and the codestream is compatible with JPEG2000 standard. Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 09/29/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Journal of Electronic Imaging 033019-2 May∕Jun 2015 • Vol. 24(3) Wang and Zhu: Background pretruncated method for JPEG2000 region of interest coding Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 09/29/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Journal of Electronic Imaging 033019-3 May∕Jun 2015 • Vol. 24(3) Wang and Zhu: Background pretruncated method for JPEG2000 region of interest coding Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 09/29/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Journal of Electronic Imaging 033019-6 May∕Jun 2015 • Vol. 24(3) Wang and Zhu: Background pretruncated method for JPEG2000 region of interest coding Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 09/29/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Journal of Electronic Imaging 033019-9 May∕Jun 2015 • Vol. 24(3) Wang and Zhu: Background pretruncated method for JPEG2000 region of interest coding Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 09/29/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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“…The Hadamard-based posttransform needs the ( log ) operations. In high bit [38] 0.102 us/sample 3DSPIHT [3] 0.062 us/sample JPEG2000 [39] 0.181 us/sample rate, we use the DCT posttransform. 2D 4 × 4 DCT requires only 27 multiplications and 231 additions.…”

Section: Proposed Algorithm Complexity Analysis and Compressionmentioning

confidence: 99%

Compression of Multispectral Images with Comparatively Few Bands Using Posttransform Tucker Decomposition

Xing

You

2014

Mathematical Problems in Engineering

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Up to now, data compression for the multispectral charge-coupled device (CCD) images with comparatively few bands (MSCFBs) is done independently on each multispectral channel. This compression codec is called a “monospectral compressor.” The monospectral compressor does not have a removing spectral redundancy stage. To fill this gap, we propose an efficient compression approach for MSCFBs. In our approach, the one dimensional discrete cosine transform (1D-DCT) is performed on spectral dimension to exploit the spectral information, and the posttransform (PT) in 2D-DWT domain is performed on each spectral band to exploit the spatial information. A deep coupling approach between the PT and Tucker decomposition (TD) is proposed to remove residual spectral redundancy between bands and residual spatial redundancy of each band. Experimental results on multispectral CCD camera data set show that the proposed compression algorithm can obtain a better compression performance and significantly outperforms the traditional compression algorithm-based TD in 2D-DWT and 3D-DCT domain.

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JPEG2000 Encoding of Remote Sensing Multispectral Images With No-Data Regions

Cited by 25 publications

References 17 publications

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

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

Background pretruncated method for JPEG2000 region of interest coding

Compression of Multispectral Images with Comparatively Few Bands Using Posttransform Tucker Decomposition

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