Compression of Multicomponent Satellite Images Using Independent Components Analysis

Recently, Narozny et al [1] 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 coding system employing entropy-constrained uniform quantization may be viewed as a modified independent component analysis (ICA) problem. By adopting this new viewpoint, two new ICA-based algorithms, called GCGsup and ICAorth, were then derived for computing respectively the optimal linear transform and the optimal orthogonal transform. In this paper, we show that the transforms returned by GCGsup and ICAorth can achieve better visual image quality (better preservation of lines and contours) than the KLT and 2-D Discrete Cosine Transform (DCT) when applied to the compression of well-known grayscale images.

show abstract

“…Furthermore, some work have already begun on the application of GCGsup and ICAorth to the compression of satellite images [9].…”

Section: Resultsmentioning

confidence: 99%

ICA-Based Algorithms Applied to Image Coding

Narozny

Barret

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

Self Cite

View full text Add to dashboard Cite

show abstract

“…They were tested on well-known gray-scale images and their performances were comparable to JPEG2000 codec while providing better visual quality. After Narozny has shown the link between ICA and transform coding, Akam-Bita applied and adapted these algorithms on multispectral and hyperspectral satellite images [15]. The procedure is almost the same as for the DWT or the KLT: the ICA transform is performed on the spectral axis and the obtained images are independent from each other.…”

Section: Pre-processing: Ica On Spectral Bandsmentioning

confidence: 99%

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

et al. 2007

View full text Add to dashboard Cite

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.

show abstract

“…Now, the criterion minimized by such an optimal coding transform without any Gaussian assumption can be expressed in terms of the mutual information between the transformed components, which is a contrast encountered in Independent Component Analysis (ICA) and for which Pham [13] developed an efficient algorithm computing the linear transform that minimizes it. Thus, the concept of optimal coding transforms (OCT), under high resolution quantization hypothesis with entropy constrained scalar quantization and mean squared distortion, has been introduced in [14,15] and adapted to multicomponent images in [8][9][10]. Among various compression schemes associating spectral linear transforms and 2-D DWT, we focused on the separable scheme [10], which is compatible with both the JP2K Part2 standard and the CCSDS recommendations for on-board satellite image compression [16].…”

Section: Introductionmentioning

confidence: 99%

“…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%

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

Barret¹

2010

J. Appl. Remote Sens

Self Cite

View full text Add to dashboard Cite

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

show abstract

Compression of Multicomponent Satellite Images Using Independent Components Analysis

Cited by 4 publications

References 8 publications

ICA-Based Algorithms Applied to Image Coding

ICA-Based Algorithms Applied to Image Coding

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

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

Contact Info

Product

Resources

About