Lossless Compression of Hyperspectral Images Using Clustered Linear Prediction With Adaptive Prediction Length

Mielikäinen, Jarno; Huang, Bormin

doi:10.1109/lgrs.2012.2191531

Cited by 49 publications

(24 citation statements)

References 7 publications

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“…In the process of encoding, one can select either sample-adaptive or block-adaptive approach to encode the prediction residuals. However, in terms of compression ratio, the Clustered Differential Pulse Code Modulation with Adaptive Prediction Length (C-DPCM-APL) [11] enhanced over C-DPCM is optimal. It achieves the highest compression ratio on the CCSDS hyperspectral images acquired in 2006.…”

Section: Previous Methodsmentioning

confidence: 99%

Lossless compression of hyperspectral images using C-DPCM-APL with reference bands selection

Wang

Liao

et al. 2014

SPIE Proceedings

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The availability of hyperspectral images has increased in recent years, which is used in military and civilian applications, such as target recognition, surveillance, geological mapping and environmental monitoring. Because of its abundant data quantity and special importance, now it exists lossless compression methods of hyperspectral images mainly exploiting the strong spatial or spectral correlation. C-DPCM-APL is a method that achieves highest lossless compression ratio on the CCSDS hyperspectral images acquired in 2006 but consuming longest processing time among existing lossless compression methods to determine the optimal prediction length for each band. C-DPCM-APL gets best compression performance mainly via using optimal prediction length but ignoring the correlationship between reference bands and the current band which is a crucial factor that influences the precision of prediction. Considering this, we propose a method that selects reference bands according to the atmospheric absorption characteristic of hyperspectral images. Experiments on CCSDS 2006 images data set show that the proposed reduces the computation complexity heavily without decaying its lossless compression performance when compared to C-DPCM-APL.

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Section: Previous Methodsmentioning

confidence: 99%

Lossless compression of hyperspectral images using C-DPCM-APL with reference bands selection

Wang

Liao

et al. 2014

SPIE Proceedings

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“…As well as low complexity specific algorithms, several high computational complexity algorithms that feature advanced pre-processing have been proposed [15] [16][17] [18]. 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.…”

Section: Lossless Image Compression Literature Reviewmentioning

confidence: 99%

“…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]. C-DPCM-APL is able to achieve an average compression ratio of 3.47, the highest compression ratio of all the algorithms surveyed.…”

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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“…The Locally Averaged Interband Scaling (LAIS)-LUT method [6] uses two LUTs per band to update and choose the closest to the LAIS as the predictor. The Clustered DPCM (C-DPCM) method is improved in [7] by using an adaptive prediction length. These prediction-based methods are successful in lossless hyperspectral image compression.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral image compression based on lapped transform and Tucker decomposition

Wang¹,

Bai²,

Wu³

et al. 2015

Signal Processing: Image Communication

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Lossless Compression of Hyperspectral Images Using Clustered Linear Prediction With Adaptive Prediction Length

Cited by 49 publications

References 7 publications

Lossless compression of hyperspectral images using C-DPCM-APL with reference bands selection

Lossless compression of hyperspectral images using C-DPCM-APL with reference bands selection

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

Hyperspectral image compression based on lapped transform and Tucker decomposition

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