Low-Complexity Hyperspectral Image Coding Using Exogenous Orthogonal Optimal Spectral Transform (OrthOST) and Degree-2 Zerotrees

Barret, Michel; Gutzwiller, Jean-Louis; Hariti, Mohamed

doi:10.1109/tgrs.2010.2083671

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…To find the estimate W j of the details components W j based on the approximation components to the Exogenous model proposed for the PCA [56] or for the orthogonal optimal spectral transform (OST) [18], [19].…”

Section: Regression Modelmentioning

confidence: 99%

See 1 more Smart Citation

Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data

Amrani

Serra-Sagristà

Laparra

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed Regression Wavelet Analysis (RWA) uses multivariate regression to exploit the relationships among wavelet-transformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion and IASI, suggest that RWA outperforms not only Principal Component Analysis (PCA) and Wavelets, but also the best and most recent coding standard in remote sensing, CCSDS-123. Index TermsTransform coding via regression, wavelet-based transform coding, remote sensing data compression, redundancy in hyperspectral images.

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Section: Regression Modelmentioning

confidence: 99%

“…Yet a third approach consists in learning the transform on a set of images of one particular sensor in order to obtain an efficient transform that can be applied to new images from the same sensor [18]- [21].…”

mentioning

confidence: 99%

Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data

Amrani

Serra-Sagristà

Laparra

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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show abstract

“…In both cases, the computational complexity is too high for a compression system on-board a satellite. In (Barret et al, 2011), the authors present a low complexity hyperspectral image coder based on exogenous OrthOST and zerotrees well adapted to OrthOST. It is important to note that the point of view presented in this chapter -i.e., a compression scheme for hyperspectral images that is independent of the end-user application -is no longer justified at very low bit-rates (lower than 0.5 bits per pixel and per band).…”

Section: Discrete Wavelet Transform and Optimal Spectral Transform Apmentioning

confidence: 99%

Discrete Wavelet Transform and Optimal Spectral Transform Applied to Multicomponent Image Coding

Bita¹,

Barret²,

Vedova³

et al. 2011

Discrete Wavelet Transforms - Algorithms and Applications

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“…Some of the embedded coding methods are based on zerotree, such as [3][4][5][37][38][39]. The zerotree-based method exploits the property of self-similarity across scales in wavelet transformed images, which construct some zerotrees by spatial similarity of the wavelet coefficients at different scales.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Compression Based on Direction Lifting-Based Block Transform with Content-Driven Quadtree Coding Adaptively

et al. 2018

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Due to the limitations of storage and transmission in remote sensing scenarios, lossy compression techniques have been commonly considered for remote sensing images. Inspired by the latest development in image coding techniques, we present in this paper a new compression framework, which combines the directional adaptive lifting partitioned block transform (DAL-PBT) with content-driven quadtree codec with optimized truncation (CQOT). First, the DAL-PBT model is designed; it calculates the optimal prediction directions of each image block and performs the weighted directional adaptive interpolation during the process of directional lifting. Secondly, the CQOT method is proposed, which provides different scanning orders among and within blocks based on image content, and encodes those blocks with a quadtree codec with optimized truncation. The two phases are closely related: the former is devoted to image representation for preserving more directional information of remote sensing images, and the latter leverages adaptive scanning on the transformed image blocks to further improve coding efficiency. The proposed method supports various progressive transmission modes. Experimental results show that the proposed method outperforms not only the mainstream compression methods, such as JPEG2000 and CCSDS, but also, in terms of some evaluation indexes, some state-of-the-art compression methods presented recently.

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Low-Complexity Hyperspectral Image Coding Using Exogenous Orthogonal Optimal Spectral Transform (OrthOST) and Degree-2 Zerotrees

Cited by 7 publications

References 22 publications

Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data

Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data

Discrete Wavelet Transform and Optimal Spectral Transform Applied to Multicomponent Image Coding

Remote Sensing Image Compression Based on Direction Lifting-Based Block Transform with Content-Driven Quadtree Coding Adaptively

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