On optimal orthogonal transforms at high bit-rates using only second order statistics in multicomponent image coding with JPEG2000

Bita, Isidore Paul Akam; Barret, Michel; Pham, Dinh-Tuan

doi:10.1016/j.sigpro.2009.08.008

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…Indeed, after the 2D DWT, the variance of the wavelet coefficients depends on the subband they belong to (even for Gaussian data) and the KLT does not capture these various variances, while the EBCOT coder with its PCRD optimizer performing simultaneously across all the codeblocks from the entire image take them into account. In (Akam Bita et al, 2010b), the authors introduced an orthogonal spectral transform (called JADO for Joint Approximate Diagonalization under Orthogonality constraint) using only second order statistics that has not this shortcoming, and that is optimal at high bit-rates for the JPEG2000 Part 2 compression scheme, when the data are Gaussian. They showed on natural hyperspectral images that JADO (resp.…”

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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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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“…Moreover, codecs based on these methods have generally a lower complexity than codecs using vectorial quantization before entropy coding. Thus, research activities on multicomponent image codecs based on transform and/or subband coding have been strengthened these last years [1][2][3][4][5][6][7][8][9][10][11]. The JPEG2000 (JP2K) Part 2 standard implements such a method with two variants for reducing spectral redundancies: by applying either a 1-D linear transform, or a 1-D discrete wavelet transform (DWT) between the components of the encoded image.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, after the 2D DWT, the variance of the wavelet coefficients depends on the subband they belong to and the KLT does not capture these various variances, while the EBCOT coder with its PCRD optimizer take them into account. In [11], the authors introduced an orthogonal spectral transform (called JADO) using only second order statistics that has not this shortcoming, and that is optimal at high bitrates for Gaussian data. They showed on natural hyperspectral images that OrthOST performs slightly but significantly better than JADO, which performs better than the KLT.…”

Section: Introductionmentioning

confidence: 99%

Lossy and lossless compression of MERIS hyperspectral images with exogenous quasi-optimal spectral transforms

Barret¹

2010

J. Appl. Remote Sens

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International audienceOur research focuses on reducing complexity of hyperspectral image codecs based on transform and/or subband coding, so they can be on-board a satellite. It is well-known that the Karhunen Loeve transform (KLT) can be sub-optimal for non Gaussian data. However, it is generally recommended as the best calculable coding transform in practice. Now, for a compression scheme compatible with both the JPEG2000 Part2 standard and the CCSDS recommendations for onboard satellite image compression, the concept and computation of optimal spectral transforms (OST), at high bit-rates, were carried out, under low restrictive hypotheses. These linear transforms are optimal for reducing spectral redundancies of multi- or hyper-spectral images, when the spatial redundancies are reduced with a fixed 2-D discrete wavelet transform. The problem of OST is their heavy computational cost. In this paper we present the performances in coding of a quasi-optimal spectral transform, called exogenous OrthOST, obtained by learning an orthogonal OST on a sample of hyperspectral images from the spectrometer MERIS. Moreover, we compute an integer variant of OrthOST for lossless compression. The performances are compared to the ones of the KLT in both lossy and lossless compressions. We observe good performances of the exogenous OrthOST

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“…Such algorithms typically suffer a small performance penalty in lossless coding performance compared to algorithms that focus exclusively on lossless coding. A common approach that achieves good performance in lossy-to-lossless coding is to utilize a one-dimensional transform to exploit the correlation between spectral bands, followed by encoding each resulting band with a lossy-to-lossless 2D image coder [2], [14], [15], [16], [17], [18].…”

mentioning

confidence: 99%

“…In case of being necessary, (18) and (19) can be trivially adapted for ultraspectral images of more than 2 16 components, at the expense of larger values of minimum Θ. For a number of levels L,…”

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confidence: 99%

Isorange Pairwise Orthogonal Transform

Blanes

Hernández-Cabronero

Aulí-Llinàs

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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Abstract-Spectral transforms are tools commonly employed in multi-and hyperspectral data compression to decorrelate images in the spectral domain. The Pairwise Orthogonal Transform (POT) is one such transform that has been specifically devised for resource-constrained contexts like those found on board satellites or airborne sensors. Combining the POT with a 2D coder yields an efficient compressor for multi-and hyperspectral data. However, a drawback of the original POT is that its dynamic range expansion -i.e., the increase in bit depth of transformed images-is not constant, which may cause problems with hardware implementations. Additionally, the dynamic range expansion is often too large to be compatible with the current 2D standard CCSDS 122.0-B-1. This paper introduces the Isorange Pairwise Orthogonal Transform, a derived transform that has a small and limited dynamic range expansion, compatible with CCSDS 122.0-B-1 in almost all scenarios. Experimental results suggest that the proposed transform achieves lossy coding performance close to that of the original transform. For lossless coding, the original POT and the proposed isorange POT achieve virtually the same performance.Index Terms-On-board multi-and hyperspectral image coding, progressive lossy-to-lossless, limited dynamic range expansion, Pairwise Orthogonal Transform (POT).

show abstract

On optimal orthogonal transforms at high bit-rates using only second order statistics in multicomponent image coding with JPEG2000

Cited by 11 publications

References 6 publications

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

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

Lossy and lossless compression of MERIS hyperspectral images with exogenous quasi-optimal spectral transforms

Isorange Pairwise Orthogonal Transform

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