Our 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.
Our 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-Loève Transform (KLT) can
be sub-optimal in transform coding for non Gaussian data. However, it is generally recommended as the best
calculable linear coding transform in practice. Now, the concept and the computation of optimal coding transforms
(OCT), under low restrictive hypotheses at high bit-rates, were carried out and adapted to a compression
scheme compatible with both the JPEG2000 Part2 standard and the CCSDS recommendations for on-board
satellite image compression, leading to the concept and computation of Optimal Spectral Transforms (OST).
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 (DWT). 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 superspectral
images from the spectrometer MERIS. The performances are presented in terms of bit-rate versus distortion for
four various distortions and compared to the ones of the KLT. We observe good performances of the exogenous
OrthOST, as it was the case on Hyperion hyper-spectral images in previous works.
Conference Committee Involvement (2)
Satellite Data Compression, Communications, and Processing VI
3 August 2010 | San Diego, California, United States
Satellite Data Compression, Communication, and Processing V
4 August 2009 | San Diego, California, United States
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