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

Zemliachenko, Alexander; Kozhemiakin, Ruslan; Uss, Mikhail; Abramov, Sergey; Ponomarenko, Nikolay N.; Lukin, Vladimir; Vozel, Benôıt; Chehdi, Kacem

doi:10.1117/1.jrs.8.083571

Cited by 46 publications

(38 citation statements)

References 51 publications

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“…According to [1], [2] and [3] the independent noise is dominant compared to signal-correlated noise, on the other hand, [4], [5] suggest that the noise is composed of both signal dependent and independent components. In a recent work of Zemliachenko et al estimated noise parameters are used in lossy compression of HSI [7]. Another type of noise which is known as photon noise is modeled by Poisson distribution in [8], the authors proposed a smoothing approach based on total variation regularization technique implemented by using Split Bregman optimization.…”

Section: Introductionmentioning

confidence: 99%

Noise estimation for hyperspectral imagery using spectral unmixing and synthesis

Demirkesen

Leloglu

2014

SPIE Proceedings

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Most hyperspectral image (HSI) processing algorithms assume a signal to noise ratio model in their formulation which makes them dependent on accurate noise estimation. Many techniques have been proposed to estimate the noise. A very comprehensive comparative study on the subject is done by Gao et al. [1]. In a nut-shell, most techniques are based on the idea of calculating standard deviation from assumed-to-be homogenous regions in the image. Some of these algorithms work on a regular grid parameterized with a window size w, while others make use of image segmentation in order to obtain homogenous regions. This study focuses not only to the statistics of the noise but to the estimation of the noise itself.A noise estimation technique motivated from a recent HSI de-noising approach [2] is proposed in this study. The denoising algorithm is based on estimation of the end-members and their fractional abundances using non-negative least squares method. The end-members are extracted using the well-known simplex volume optimization technique called N-FINDR after manual selection of number of end-members and the image is reconstructed using the estimated endmembers and abundances. Actually, image de-noising and noise estimation are two sides of the same coin: Once we denoise an image, we can estimate the noise by calculating the difference of the de-noised image and the original noisy image.In this study, the noise is estimated as described above. To assess the accuracy of this method, the methodology in [1] is followed, i.e., synthetic images are created by mixing end-member spectra and noise. Since best performing method for noise estimation was spectral and spatial de-correlation (SSDC) originally proposed in [3], the proposed method is compared to SSDC. The results of the experiments conducted with synthetic HSIs suggest that the proposed noise estimation strategy outperforms the existing techniques in terms of mean and standard deviation of absolute error of the estimated noise. Finally, it is shown that the proposed technique demonstrated a robust behavior to the change of its single parameter, namely the number of end-members.

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Section: Introductionmentioning

confidence: 99%

Noise estimation for hyperspectral imagery using spectral unmixing and synthesis

Demirkesen

Leloglu

2014

SPIE Proceedings

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“…One assumption is that introduced losses have to be of the same level or smaller than degradations due to noise in original data [6]. Therefore, noise characteristics have to be taken into consideration and, thus, they should be known in advance or pre-estimated [7][8][9][10][11]. This also means that it is necessary to be able to control introduced distortions and/or to provide a desired level of losses.…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, it is worth incorporating inter-channel correlation inherent for multichannel RS data that can be done in different ways [19][20][21]. It is possible to apply different transforms [11,[22][23][24] or to carry out different groupings of component images [11,25,26].…”

Section: Introductionmentioning

confidence: 99%

Lossy Compression of Remote Sensing Images with Controllable Distortions

Zemliachenko¹,

Krivenko²,

Vozel³

et al. 2019

Satellite Information Classification and Interpretation

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In this chapter, approaches to provide a desired quality of remote sensing images compressed in a lossy manner are considered. It is shown that, under certain conditions, this can be done automatically and quickly using prediction of coder performance parameters. The main parameters (metrics) are mean square error (MSE) or peak signal-to-noise ratio (PSNR) of introduced losses (distortions) although prediction of other important metrics is also possible. Having such a prediction, it becomes possible to set a quantization step of a coder in a proper manner to provide distortions of a desired level or less without compression/decompression iterations for single-channel image. It is shown that this approach can be also exploited in three-dimensional (3D) compression of multichannel images to produce a larger compression ratio (CR) for the same or less introduced distortions as for component-wise compression of multichannel data. The proposed methods are verified for test and real life images.

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“…There are methods for estimation of characteristics of such multiplicative or non-Gaussian noises [34][35][36][37][38][39][40], mixed additive and multiplicative noise [39]. One should note that in some cases, a problem of estimation of parameters of multiplicative noise may by replaced by estimation of variance of additive noise by preliminary usage to analyzed images of homomorphic or variance stabilizing transforms [41][42][43], such as Anscombe transform [44].…”

Section: Introductionmentioning

confidence: 99%

Blind estimation of white Gaussian noise variance in highly textured images

Ponomarenko¹,

Gapon²,

Voronin³

et al. 2018

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In the paper, a new method of blind estimation of noise variance in a single highly textured image is proposed. An input image is divided into 8x8 blocks and discrete cosine transform (DCT) is performed for each block. A part of 64 DCT coefficients with lowest energy calculated through all blocks is selected for further analysis. For the DCT coefficients, a robust estimate of noise variance is calculated. Corresponding to the obtained estimate, a part of blocks having very large values of local variance calculated only for the selected DCT coefficients are excluded from the further analysis. These two steps (estimation of noise variance and exclusion of blocks) are iteratively repeated three times. For the verification of the proposed method, a new noise-free test image database TAMPERE17 consisting of many highly textured images is designed. It is shown for this database and different values of noise variance from the set {25, 49, 100, 225}, that the proposed method provides approximately two times lower estimation root mean square error than other methods.

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

Cited by 46 publications

References 51 publications

Noise estimation for hyperspectral imagery using spectral unmixing and synthesis

Noise estimation for hyperspectral imagery using spectral unmixing and synthesis

Lossy Compression of Remote Sensing Images with Controllable Distortions

Blind estimation of white Gaussian noise variance in highly textured images

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