ICA-Based Algorithms Applied to Image Coding

Narozny, Michel; Barret, Michel

doi:10.1109/icassp.2007.366087

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Recently, Narozny et al [13] proposed a new viewpoint in variable high-rate transform coding. They showed that the problem of finding the optimal 1-D linear block transform for a high-rate transform coding system employing entropy constrained uniform quantization may be viewed as a modified ICA problem.…”

Section: Pre-processing: Ica On Spectral Bandsmentioning

confidence: 99%

CNES studies of on-board compression for multispectral and hyperspectral images

et al. 2007

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Future high resolution instruments planned by CNES for space remote sensing missions will lead to higher bit rates because of the increase in resolution, dynamic range and number of spectral channels for multispectral (up to 16 bands) and hyperspectral (hundreds of bands) imagery. Lossy data compression is then needed, with compression ratio goals always higher and with low-complexity algorithm. For optimum compression performance of such data, algorithms must exploit both spectral and spatial correlation. In the case of multispectral images, CNES (in cooperation with Thales Alenia Space, hereafter TAS) studies have led to an algorithm using a fixed transform to decorrelate the spectral bands, the CCSDS codec compresses each decorrelated band using a suitable multispectral rate allocation procedure. This lowcomplexity decorrelator is adapted to hardware implementation on-board satellite and is under development. In the case of hyperspectral images, CNES (in cooperation with TAS/TeSA/ONERA) studies have led to a full wavelet compression system followed by zerotree coding methods adapted to this decomposition. We are investigating other preprocessors such as Independent Component Analysis which could be used in both approaches. CNES also participates to the new CCSDS Multispectral and Hyperspectral Data Compression Working Group.

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Section: Pre-processing: Ica On Spectral Bandsmentioning

confidence: 99%

CNES studies of on-board compression for multispectral and hyperspectral images

et al. 2007

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“…The paper begins with a brief review of high bit-rate transform coding. The results presented here have been partially published in [15] and [16].…”

Section: Introductionmentioning

confidence: 99%

ICA based algorithms for computing optimal 1-D linear block transforms in variable high-rate source coding

2008

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The Karhunen-Loève Transform (KLT) is optimal for transform coding of Gaussian sources, however it is no more optimal, in general, for non Gaussian sources. Furthermore, under the high resolution quantization hypothesis, nearly everything is known about the performance of a transform coding with entropy constrained scalar quantization and mean square distortion. It is then straightforward to find a criterion that, when minimized, gives the optimal linear transform under the above mentioned conditions. However, the optimal transform computation is generally considered as a difficult task and the Gaussian assumption is then used in order to simplify the calculus. In this paper, we present the above mentioned criterion as a contrast of Independent Component Analysis modified by an additional term which is a penalty to nonorthogonality. Then we adapt the icainf algorithm by Pham in order to compute the transform minimizing the criterion either with no constraint or with the orthogonality constraint. Finally, experimental results show that the transforms we introduced can 1) outperform the KLT on synthetic signals, 2) achieve slightly better PSNR for high-rates and better visual quality (preservation of lines and contours) for medium-to-low rates than the KLT and 2-D DCT on grayscale natural images.

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Adaptive coding of images via multiresolution ICA

Balcan

Lewicki

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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ICA-Based Algorithms Applied to Image Coding

Cited by 3 publications

References 9 publications

CNES studies of on-board compression for multispectral and hyperspectral images

CNES studies of on-board compression for multispectral and hyperspectral images

ICA based algorithms for computing optimal 1-D linear block transforms in variable high-rate source coding

Adaptive coding of images via multiresolution ICA

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