Lossless compression of hyperspectral images based on contents

Yi, 汤毅 Tang; Qin, 辛勤 Xin; Gang, 李纲 Li; Jianwei, 万建伟 Wan

doi:10.3788/ope.20122003.0668

Cited by 4 publications

(3 citation statements)

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“…Magli also introduced Kalman filter to implement the spectral linear prediction [4]. Tang et al introduced ground classification to improve the lossless compression performance [5], which has high encoder complexity and poor error resilience. Distributed source coding (DSC) has received increased attention in the past few years and has provided separate encoding and joint decoding, which moves the computational complexity from the encoder to the decoder, thus meeting the requirements of onboard compression [6,7].…”

Section: Introductionmentioning

confidence: 99%

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

Nian

Wan

2013

Mathematical Problems in Engineering

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A low-complexity compression algorithm for hyperspectral images based on distributed source coding (DSC) is proposed in this paper. The proposed distributed compression algorithm can realize both lossless and lossy compression, which is implemented by performing scalar quantization strategy on the original hyperspectral images followed by distributed lossless compression. Multilinear regression model is introduced for distributed lossless compression in order to improve the quality of side information. Optimal quantized step is determined according to the restriction of the correct DSC decoding, which makes the proposed algorithm achieve near lossless compression. Moreover, an effective rate distortion algorithm is introduced for the proposed algorithm to achieve low bit rate. Experimental results show that the compression performance of the proposed algorithm is competitive with that of the state-of-the-art compression algorithms for hyperspectral images.

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Section: Introductionmentioning

confidence: 99%

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

Nian

Wan

2013

Mathematical Problems in Engineering

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“…OCC has well-preserved transform information and can achieve higher rate-distortion performance than PCA-based compression techniques while applying different existing encoder techniques. Tang, Xing, Li and Wang [ 9 ] use an adaptive band selection to reduce dimensionality. In this method, adjacent bands are arranged into one group and compressed using the JPEG-LS standard.…”

Section: Introductionmentioning

confidence: 99%

Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

et al. 2016

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A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times the data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing “original pixel intensity”-based coding approaches using traditional image coders (e.g., JPEG2000) to the “residual”-based approaches using a video coder for better compression performance. A modified video coder is required to exploit spatial-spectral redundancy using pixel-level reflectance modelling due to the different characteristics of HS images in their spectral and shape domain of panchromatic imagery compared to traditional videos. In this paper a novel coding framework using Reflectance Prediction Modelling (RPM) in the latest video coding standard High Efficiency Video Coding (HEVC) for HS images is proposed. An HS image presents a wealth of data where every pixel is considered a vector for different spectral bands. By quantitative comparison and analysis of pixel vector distribution along spectral bands, we conclude that modelling can predict the distribution and correlation of the pixel vectors for different bands. To exploit distribution of the known pixel vector, we estimate a predicted current spectral band from the previous bands using Gaussian mixture-based modelling. The predicted band is used as the additional reference band together with the immediate previous band when we apply the HEVC. Every spectral band of an HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream encoders. The experimental results are fully justified by three types of HS dataset with different wavelength ranges. The proposed method outperforms the existing mainstream HS encoders in terms of rate-distortion performance of HS image compression.

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“…At present, however, practical on-board compression algorithms of multispectral or hyperspectral images have not taken the spectral correlation into account, so the compression efficiency is limited in despite of the low encoder complexity. Classical compression algorithms are all based on joint encoding and decoding structures [2][3][4]. Though their compression efficiency is perfect, prevalent are the shortages such as high encoder and decoder complexity and weak error resilience.…”

Section: Introductionmentioning

confidence: 99%

Near lossless compression of hyperspectral images based on distributed source coding

Nian

Wan

Tang

et al. 2012

Sci. China Inf. Sci.

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Effective compression technique of on-board hyperspectral images has been an active topic in the field of hyperspectral remote sensintg. In order to solve the effective compression of on-board hyperspectral images, a new distributed near lossless compression algorithm based on multilevel coset codes is proposed. Due to the diverse importance of each band, a new adaptive rate allocation algorithm is proposed, which allocates rational rate for each band according to the size of weight factor defined for hyperspectral images subject to the target rate constraints. Multiband prediction is introduced for Slepian-Wolf lossless coding and an optimal quantization algorithm is presented under the correct reconstruction of Slepian-Wolf decoder, which minimizes the distortion of reconstructed hyperspectral images under the target rate. Then Slepian-Wolf encoder exploits the correlation of the quantized values to generate the final bit streams. Experimental results show that the proposed algorithm has both higher compression efficiency and lower encoder complexity than several existing classical algorithms.

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Lossless compression of hyperspectral images based on contents

Cited by 4 publications

References 0 publications

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

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

Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

Near lossless compression of hyperspectral images based on distributed source coding

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