Clustered dpcm for the lossless compression of hyperspectral images

Mielikäinen, Jarno; Toivanen, Pekka

doi:10.1109/tgrs.2003.820885

Cited by 111 publications

(47 citation statements)

References 15 publications

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“…These algorithms are able to achieve state-of-the-art levels of compression due to the incorporation of image segmentation to determine areas of homogenous pixels for increased prediction accuracy. Clustered Differential Pulse Code Modulation (C-DPCM) is an example of one such algorithm whereby error optimised linear predictors are calculated for each cluster utilising collocated pixels from previously encoded bands [17]. C-DPCM-APL (Adaptive Predictor Length), is a variation of this algorithm that uses a brute force approach to determine the optimum number of previously encoded bands to use in the linear predictor calculation [18].…”

Section: Lossless Image Compression Literature Reviewmentioning

confidence: 99%

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

Davidson

Bridges

2016

2016 IEEE International Conference on Imaging Systems and Techniques (IST)

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Abstract-In recent years the growth in quantity, diversity and capability of Earth Observation (EO) satellites, has enabled increase's in the achievable payload data dimensionality and volume. However, the lack of equivalent advancement in downlink technology has resulted in the development of an onboard data bottleneck. This bottleneck must be alleviated in order for EO satellites to continue to efficiently provide high quality and increasing quantities of payload data.This research explores the selection and implementation of state-of-the-art multidimensional image compression algorithms and proposes a new onboard data processing architecture, to help alleviate the bottleneck and increase the data throughput of the platform. The proposed new system is based upon a backplane architecture to provide scalability with different satellite platform sizes and varying mission's objectives. The heterogeneous nature of the architecture allows benefits of both Field Programmable Gate Array (FPGA) and Graphical Processing Unit (GPU) hardware to be leveraged for maximised data processing throughput.

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Section: Lossless Image Compression Literature Reviewmentioning

confidence: 99%

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

Davidson

Bridges

2016

2016 IEEE International Conference on Imaging Systems and Techniques (IST)

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“…Predictive coding requires intensive computation and relies on extensive side information to implement the sophisticated predictors such as clustered differential pulse-code modulation (C-DPCM) [13] and context-based adaptive lossless image coding (CALIC) [14,15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods

Elhannachi¹,

Benamrane²,

Abdelmalik³

2017

J Inf Process Syst

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Since the progress of digital medical imaging techniques, it has been needed to compress the variety of medical images. In medical imaging, reversible compression of image's region of interest (ROI) which is diagnostically relevant is considered essential. Then, improving the global compression rate of the image can also be obtained by separately coding the ROI part and the remaining image (called background). For this purpose, the present work proposes an efficient reversible discrete cosine transform (RDCT) based embedded image coder designed for lossless ROI coding in very high compression ratio. Motivated by the wavelet structure of DCT, the proposed rearranged structure is well coupled with a lossless embedded zerotree wavelet coder (LEZW), while the background is highly compressed using the set partitioning in hierarchical trees (SPIHT) technique. Results coding shows that the performance of the proposed new coder is much superior to that of various state-of-art still image compression methods.

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“…Through prediction using optimal multibands, a more efficient scheme for lossless compression was presented by Huo et al (Huo, Zhang, and Peng 2009), with a higher compression ratio of 3.3:1 achieved. In addition, there are also some other approaches introduced, using techniques such as clustered DPCM (differential pulse code modulation) coding (Mielikainen and Toivanen 2003), lookup tables (LUT) (Mielikainen and Toivanen 2008), crisp and fuzzy adaptive spectral predictions (Aiazzi et al 2007), context-based adaptive classified arithmetic coding in wavelet domain (Zhang and Liu 2007b) and reordering prediction (Zhang and Liu 2007a). Although higher compression ratios can be reached by such approaches, the cost of complex computations they require seems unaffordable for real-time applications.…”

Section: Introductionmentioning

confidence: 99%

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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Clustered dpcm for the lossless compression of hyperspectral images

Cited by 111 publications

References 15 publications

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods

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

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