VST-based lossy compression of hyperspectral data for new generation sensors

Zemliachenko, Alexander; Kozhemiakin, Ruslan; Uss, Mikhail; Abramov, Sergey; Vozel, Benôıt; Chehdi, Kacem

doi:10.1117/12.2028415

Cited by 4 publications

(8 citation statements)

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“…In fact, this paper extends initial results presented in a conference paper, 25 but with distinctive differences mentioned below. Section 2 considers some peculiarities of hyperspectral data for the Hyperion sensor and presents the estimates of SD noise parameters.…”

Section: Introductionsupporting

confidence: 67%

“…Compared to the paper, 25 more real-life data sets are considered to strengthen the conclusion drawn from an analysis of the results and to make these conclusions more general.…”

Section: Introductionmentioning

confidence: 99%

“…We present more practical examples in Ref. 25 to better stress the potentiality of this approach. Section 6 absolutely contains new results dealing with the 3-D compression of data after VST in groups.…”

Section: Introductionmentioning

confidence: 99%

“…They are tested and compared for simulated data (test images). Compared to the paper, 25 we give more details and provide a more thorough analysis on coder parameter setting in order to reach compression in an OOP neighborhood. In particular, novelty consists in an analysis of different images for approaches to lossy compression.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, there are also several papers where the authors have already done several steps in this direction. 16,[19][20][21][22][24][25][26] Obviously, to take noise statistics into account, it is necessary to have it at disposal or to estimate the statistics with appropriate accuracy. Such estimation has to be performed either from calibration data (if available) or from obtained RS data.…”

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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Section: Introductionsupporting

confidence: 67%

“…Compared to the paper, 25 more real-life data sets are considered to strengthen the conclusion drawn from an analysis of the results and to make these conclusions more general.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Image quality prediction for DCT-based compression

Kozhemiakin

Vozel

2017

2017 14th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM)

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Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters

Zemliachenko

Abramov

Lukin

et al. 2015

J. Appl. Remote Sens

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We address lossy compression of noisy remote sensing images, where the noise is supposed to be spatially uncorrelated (white), additive originally or after a proper variancestabilizing transformation (VST). In such situations, the so-called optimal operation point (OOP) might exist. The OOP is associated with the parameter that controls compression (e.g., quantization step) for which the compressed image is the closest to the noise-free image according to a certain criterion and is closer than the original noisy image. Lossy compression in the neighborhood of OOP, if it exists, relates to an essential noise filtering effect and some distortions. Then such lossy compression (in the neighborhood of OOP) becomes expedient. However, it may be that OOP does not exist for a given image and the observed noise intensity. In such a situation, it can be reasonable to carry out a more "careful" image compression (with a lower compression ratio). Also, it is expedient to predict the existence of OOP and the compression parameters at this point in advance in order to perform adaptive and automated compression. The OOP existence that can be predicted for some coders based on the discrete cosine transform (DCT) is shown. The proposed prediction procedure is simple and fast. It presumes the calculation of DCT coefficient statistics in nonoverlapping 8 × 8 pixel blocks for a given image and uses an approximating curve obtained in advance. It is shown that it is possible to predict values for both conventional metrics, such as mean square error or peak-signal-to-noise ratio, and some visual quality metrics for the coder parameters that correspond to a possible OOP. The designed prediction procedure is tested on Hyperion and AVIRIS hyperspectral remote sensing data.

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VST-based lossy compression of hyperspectral data for new generation sensors

Cited by 4 publications

References 0 publications

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

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

Image quality prediction for DCT-based compression

Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters

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