Compression of Spectral Images

Kaarna, Arto

doi:10.5772/4964

Cited by 18 publications

(9 citation statements)

References 83 publications

(107 reference statements)

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“…For most of such multichannel data, an inherent feature is the presence of considerable correlation between components (subbands, channels, frames) [6,19,[34][35]. This correlation is exploited in vector filtering of color and multi-spectral images [6,19,20,34] and in compression of multichannel data [35] and video (note that it is impossible to take advantage of this correlation in the case of component-wise filtering of multichannel data).…”

Section: Current Problems In Image Filteringmentioning

confidence: 99%

Image filtering: Potential efficiency and current problems

Abramov

Ponomarenko

Егиазарян

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The paper contains comparisons of lower bound (potential) and achieved efficiency for filtering grayscale and color images corrupted by AWGN. It is demonstrated that for complex structure images the corresponding limits are practically reached. Then, the main problems of the current stage of filter design are discussed taking into account noise models more adequate for practice, inherent properties of multicomponent (e.g. color, hyperspectral, etc.) images, and other qualitative criteria than conventional MSE or PSNR criteria.

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Section: Current Problems In Image Filteringmentioning

confidence: 99%

Image filtering: Potential efficiency and current problems

Abramov

Ponomarenko

Егиазарян

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…This is due to very good data de-correlation and the energy compaction properties of the DCT, which are widely exploited in image and video compression. 25 Efficiency and usefulness of the local DCT commonly carried out in 8 × 8 pixel blocks has also been proven for image denoising applications in Refs. [26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Wiener discrete cosine transform-based image filtering

Pogrebnyak¹

2012

J. Electron. Imaging

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Abstract. A classical problem of additive white (spatially uncorrelated) Gaussian noise suppression in grayscale images is considered. The main attention is paid to discrete cosine transform (DCT)-based denoising, in particular, to image processing in blocks of a limited size. The efficiency of DCT-based image filtering with hard thresholding is studied for different sizes of overlapped blocks. A multiscale approach that aggregates the outputs of DCT filters having different overlapped block sizes is proposed. Later, a two-stage denoising procedure that presumes the use of the multiscale DCT-based filtering with hard thresholding at the first stage and a multiscale Wiener DCT-based filtering at the second stage is proposed and tested. The efficiency of the proposed multiscale DCT-based filtering is compared to the state-of-the-art block-matching and three-dimensional filter. Next, the potentially reachable multiscale filtering efficiency in terms of output mean square error (MSE) is studied. The obtained results are of the same order as those obtained by Chatterjee's approach based on nonlocal patch processing. It is shown that the ideal Wiener DCTbased filter potential is usually higher when noise variance is high.

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“…1,4,5 Meanwhile, this inherent property allows increasing CR by employing spectral redundancy of the data without increasing losses. 4,5,13,[38][39][40] There are numerous possible approaches to carry out 3-D compression. Our goal here is not to compare them and/or to find the best (optimal) approach.…”

Section: Compression Of Difference Images With Sub-band Groupingmentioning

confidence: 99%

“…The first stage is to estimate the noise parameters k and σ 2 0 in a blind manner (if these parameters are not at disposal). Then, an image is transformed according to (5) and, if needed, stretched to a desired range. After this, QS or SF are determined according to (6) or (7) depending upon whether its stretching is used or not.…”

Section: Comparison Of Approaches To Component-wise Lossy Compressionmentioning

confidence: 99%

“…6 However, lossless compression techniques applicable to hyperspectral data have a serious drawback. Even the best among them provide a compression ratio (CR) of about 4-5, 5,6,8 and there are no recent papers demonstrating considerable improvement of CR for lossless compression techniques. Meanwhile, it is often desirable or required to have a larger CR.…”

Section: Introductionmentioning

confidence: 99%

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Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Zemliachenko

Kozhemiakin

Uss

et al. 2014

J. Appl. Remote Sens

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Abstract. A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and "difference" images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15-20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.

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Compression of Spectral Images

Cited by 18 publications

References 83 publications

Image filtering: Potential efficiency and current problems

Image filtering: Potential efficiency and current problems

Wiener discrete cosine transform-based image filtering

Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

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