Low-Complexity Compression Method for Hyperspectral Images Based on Distributed Source Coding

Pan, Xuzhou; Liu, Rongke; Lv, Xiaoqian

doi:10.1109/lgrs.2011.2165271

Cited by 39 publications

(13 citation statements)

References 12 publications

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“…In the paper, both DCT and DWT based 3D approaches are investigated, where a modified 3D DCT based approach is proposed and compared with 3D SPIHT algorithm. Although it was found DWT usually outperforms DCT in this context (Penna et al 2007;Xiong et al 1999), better results from DCT were reported by others (Pan, Liu, and Lv 2012). Therefore, we also aim to evaluate these two approaches in terms of the compression performance and any sideeffects on subsequent data analysis.…”

Section: Introductionmentioning

confidence: 93%

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Qiao

Ren

Sun

et al. 2014

International Journal of Remote Sensing

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This version is available at https://strathprints.strath.ac.uk/50084/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Effective compression of hyperspectral imagery using an improved 3DDCT approach for land cover analysis in remote sensing applications Effective compression of hyperspectral imagery using an improved 3D DCT approach for land cover analysis in remote sensing applicationsAlthough hyperspectral imagery (HSI), which has been applied in a wide range of applications, suffers from very large volumes of data, its uncompressed representation is still preferred to avoid compression loss for accurate data analysis. In this paper, we focus on quality-assured lossy compression of HSI,where the accuracy of analysis from decoded data is taken as a key criterion to

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Section: Introductionmentioning

confidence: 93%

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Qiao

Ren

Sun

et al. 2014

International Journal of Remote Sensing

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“…Both schemes reported in [25] and [26] utilize DCT to remove the spatial redundancy at the encoder, which will cancel out some of the complexity reductions gained from DSC coding. Although [27] exploits the spatial and spectral correlation at the decoder side, its prediction performance has suffered because of its unrealistic assumption that all the pixels in 16 16 blocks have the same spectral correlation and can be predicted with the same linear filter.…”

Section: A Prior Workmentioning

confidence: 99%

Distributed Lossless Coding Techniques for Hyperspectral Images

Zhang

Chen

2015

IEEE J. Sel. Top. Signal Process.

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In this paper, we present a novel distributed coding scheme for lossless, progressive and low complexity compression of hyperspectral images. Hyperspectral images have several unique requirements that are vastly different from consumer images. Among them, lossless compression, progressive transmission, and low complexity onboard processing are three most prominent ones. To satisfy these requirements, we design a distributed coding scheme that shifts the complexity of data decorrelation to the decoder side to achieve lightweight onboard processing after image acquisition. At the encoder, the images are subsampled in order to facilitate successive encoding and progressive transmission. At the decoder, we generate the side information with adaptive region-based predictor by taking full advantage of the decoded subsampled images and previously decoded neighboring bands based on the assumptions that the objects appearing in different bands are highly correlated. The proposed progressive transmission via subsampling enables the spectral correlation to be refined successively, resulting in gradually improved decoding performance of higher-resolution layers as more sub-images are decoded. Experimental results on the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data demonstrate that the proposed scheme is able to achieve competitive compression performance comparing with the-state-of-the-art 3D schemes, including existing distributed source coding (DSC) schemes. The proposed scheme has even lower encoding complexity than that of the conventional 2D schemes.

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“…Unlike videos, ME is not necessary here since different spectral bands are responses of the same object in different wavelengths [44]. Instead, a linear prediction from decoded bands is often assumed to generate the SI [24]- [27]. Estimating SI at the encoder allows for near lossless coding for coset codes [25], [26] and optimal quantization for rate allocation [25].…”

Section: B Distributed Source Codingmentioning

confidence: 99%

“…Estimating SI at the encoder allows for near lossless coding for coset codes [25], [26] and optimal quantization for rate allocation [25]. By transmitting coefficients of the linear prediction model, a more accurate SI can be obtained [27]. Exchanging parts of data with adjacent bands makes it possible to estimate the rate at the encoder and does not decrease the parallelism [45].…”

Section: B Distributed Source Codingmentioning

confidence: 99%

“…This just fits in our scenario that the correlated images are available only in the cloud. Nonetheless, although widely studied in low-complexity distributed video coding (DVC) [13]- [15], distributed multiview video coding (DMVC) [16]- [23], distributed multispectral image coding [24]- [27], etc., it has hardly been used for cloud-based image coding before. Compared with existing distributed video/image coding, cloud-based image coding faces more challenges.…”

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confidence: 99%

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Cloud-Based Distributed Image Coding

Song

Peng

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

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With the flourish of multimedia on the web, it is easy to find similar images for a query, especially landmark images. Traditional image coding such as JPEG cannot exploit correlations with external images. Existing vision-based approaches by reconstructing from local descriptors are able to exploit such correlations but cannot ensure the pixel-level fidelity of the reconstruction. In this paper, a cloud-based distributed coding scheme (Cloud-DIC) is proposed to exploit external correlations for mobile photo uploading. For each input image, a thumbnail is transmitted to retrieve correlated images and reconstruct it in the cloud by geometrical and illumination registrations. Such a reconstruction serves as the side information (SI) in the Cloud-DIC. The image is then compressed by a transform-domain syndrome coding to correct the disparity between the original image and the SI. Once a bitplane is received in the cloud, an iterative refinement process is performed between the final reconstruction and the SI. Moreover, a joint encoder/decoder mode decision at block, frequency and bitplane levels is proposed to adapt to different correlations. Experiment results on a landmark image database show that the Cloud-DIC can largely enhance the coding efficiency both subjectively and objectively with up to 5dB gains and 70% bits saving over JPEG with arithmetic coding and perform comparably at low bit rates with HEVC intra coding with a much lower encoder complexity.Index Terms-Cloud-based coding, distributed image coding, local feature descriptors.

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Low-Complexity Compression Method for Hyperspectral Images Based on Distributed Source Coding

Cited by 39 publications

References 12 publications

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Distributed Lossless Coding Techniques for Hyperspectral Images

Cloud-Based Distributed Image Coding

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