Constant SNR, Rate Control, and Entropy Coding for Predictive Lossy Hyperspectral Image Compression

Conoscenti, Marco; Coppola, Riccardo; Magli, Enrico

doi:10.1109/tgrs.2016.2603998

Cited by 32 publications

(23 citation statements)

References 38 publications

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“…When compared to a prediction-based approach, our inpainting scheme is superior to [4] at high compression ratios, usually even up to a bit-rate of 1 bpppc, while at moderate to low compression ratios, our approach lags behind, a prevalent behaviour when comparing transform-based and prediction-based approaches [17], [18].…”

Section: E Known Data Distributionmentioning

confidence: 89%

“…Figure 4 provides the rate-distortion performance for the transform-based methods applied to the Hawaii uncalibrated image. Table I reports the results of our algorithm as compared to [4]. The rate-distortion performance is assessed through the relation between the target bitrate, measured in bits per pixel per component (bpppc), and the reconstruction …”

Section: E Known Data Distributionmentioning

confidence: 99%

“…Nevertheless, the predictive methods gain more importance for lossless and near-lossless coding. For instance, the M-CALIC algorithm [2], the standard CCSDS-123.0 [3] and a recent proposed extension of CCSDS-123.0 [4] can outperform the transform-based methods for high-quality and lossless coding. Other recently proposed works combine transform-and predictive-based methods using regression analysis to tackle the dependencies that still remain among the data in the transform domain.…”

Section: Introductionmentioning

confidence: 99%

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Diffusion-Based Inpainting for Coding Remote-Sensing Data

Amrani

Serra-Sagristà

Peter

et al. 2017

IEEE Geosci. Remote Sensing Lett.

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Abstract-Inpainting techniques based on partial differential equations (PDEs) such as diffusion processes are gaining growing importance as a novel family of image compression methods. Nevertheless, the application of inpainting in the field of hyperspectral imagery has been mainly focused on filling in missing information or dead pixels due to sensor failures. In this paper we propose a novel PDE-based inpainting algorithm to compress hyperspectral images. The method inpaints separately the known data in the spatial and in the spectral dimensions. Then it applies a prediction model to the final inpainting solution to obtain a representation much closer to the original image. Experimental results over a set of hyperspectral images indicate that the proposed algorithm can perform better than a recent proposed extension to prediction-based standard CCSDS-123.0 at low bitrate, better than JPEG 2000 Part 2 with the DWT 9/7 as a spectral transform at all bit-rates, and competitive to JPEG 2000 with principal component analysis (PCA), the optimal spectral decorrelation transform for Gaussian sources.

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Section: E Known Data Distributionmentioning

confidence: 89%

Section: E Known Data Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diffusion-Based Inpainting for Coding Remote-Sensing Data

Amrani

Serra-Sagristà

Peter

et al. 2017

IEEE Geosci. Remote Sensing Lett.

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“…Moreover, as long as the radiometric resolution of the image increases, expressed as number of bit per pixel, lossy approaches obtain better results than lossless techniques, in terms of quality of reconstructed images. In the literature, several lossy approaches have been proposed for the compression of HS images (Abousleman et al, 1995;Conoscenti, Coppola, & Magli, 2016;Fowler et al, 2007;Karami, Heylen, & Scheunders, 2015;Kulkarni et al, 2006). Many of these techniques are based on decorrelation transforms, in order to exploit both spatial and spectral correlations, followed by a quantization stage and an entropy coder.…”

Section: Introductionmentioning

confidence: 99%

Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images

Licciardi

Chanussot

2018

European Journal of Remote Sensing

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Managing transmission and storage of hyperspectral (HS) images can be extremely difficult. Thus, the dimensionality reduction of HS data becomes necessary. Among several dimensionality reduction techniques, transform-based have found to be effective for HS data. While spatial transformation techniques provide good compression rates, the choice of the spectral decorrelation approaches can have strong impact on the quality of the compressed image. Since HS images are highly correlated within each spectral band and in particular across neighboring bands, the choice of a decorrelation method allowing to retain as much information content as possible is desirable. From this point of view, several methods based on PCA and Wavelet have been presented in the literature. In this paper, we propose the use of NLPCA transform as a method to reduce the spectral dimensionality of HS data. NLPCA represents in a lower dimensional space the same information content with less features than PCA. In these terms, aim of this research is focused on the analysis of the results obtained through the spectral decorrelation phase rather than taking advantage of both spectral and spatial compression. Experimental results assessing the advantage of NLPCA with respect to standard PCA are presented on four different HS datasets. ARTICLE HISTORY

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“…Hence, developing real-time and efficient approaches is urgently needed. Based on that point, some research started to develop on-board computing approaches [26][27][28][29], where the data compression could be carried out on satellites to reduce the demand of downlink bandwidth, focusing additionally on real-time image classification with the aid of GPU acceleration [30,31].…”

Section: Introductionmentioning

confidence: 99%

Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

et al. 2018

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Band selection (BS) is one of the important topics in hyperspectral image (HSI) processing. Many types of BS algorithms were proposed in the last decade. However, most of them were designed for off-line use. They can only be used with pre-collected data, and are sometimes ineffective for applications that require timeliness, such as disaster prevention or target detection. This paper proposes an online BS method that allows us obtain instant BS results in a progressive manner during HSI data transmission, which is carried out under band-interleaved-by-sample/pixel (BIS/BIP) format. Such a revolutionary method is called progressive sample processing of band selection (PSP-BS). In PSP-BS, BS can be done recursively pixel by pixel, so that the instantaneous BS can be achieved without waiting for all the pixels of an image. To develop a PSP-BS algorithm, we proposed PSP-OMPBS, which adopted the recursive version of a self-sparse regression BS method (OMPBS) as a native algorithm. The experiments conducted on two real hyperspectral images demonstrate that PSP-OMPBS can progressively output the BS with extremely low computing time. In addition, the convergence of BS results during transmission can be further accelerated by using a pre-defined pixel transmission sequence. Such a significant advantage not only allows BS to be done in a real-time manner for the future satellite data downlink, but also determines the BS results in advance, without waiting to receive every pixel of an image.

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Constant SNR, Rate Control, and Entropy Coding for Predictive Lossy Hyperspectral Image Compression

Abstract: This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

Cited by 32 publications

References 38 publications

Diffusion-Based Inpainting for Coding Remote-Sensing Data

Diffusion-Based Inpainting for Coding Remote-Sensing Data

Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images

Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

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