Color image lossy compression based on blind evaluation and prediction of noise characteristics

Ponomarenko, Nikolay N.; Егиазарян, Карен; Lepistö, Leena

doi:10.1117/12.872009

Cited by 10 publications

(14 citation statements)

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“…These methods can be applied if noise variance in color components is not known in advance creating the basis for fully automatic processing [58]. If noise in color images has specific properties described in Section 1 and the articles [18,39], we recommend using in blind estimation of noise variance only the image fragments (blocks, scanning windows) with local mean from 25 till 230.…”

Section: Potential Limits and Preliminary Analysis Of Filter Efficiencymentioning

confidence: 99%

“…The noise in original (raw) images is clearly signal dependent [1,21,38]. After nonlinear operations with data in image processing chain [2], the assumption on noise Gaussianity and approximately constant variance of noise holds only for component image fragments with local mean intensity from about 20 till about 230...235 [18,39]. Moreover, even for such fragments, noise variance can slightly differ for R, G, and B components where for G component it is usually the smallest.…”

Section: Introductionmentioning

confidence: 99%

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Efficiency analysis of color image filtering

Fevralev

Ponomarenko

Lukin

et al. 2011

EURASIP J. Adv. Signal Process.

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This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.

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Section: Potential Limits and Preliminary Analysis Of Filter Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficiency analysis of color image filtering

Fevralev

Ponomarenko

Lukin

et al. 2011

EURASIP J. Adv. Signal Process.

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“…Proper thresholds in edge detection and image segmentation depend on noise statistics as well [1,10]. In lossy image compression, a quantization step has to be adaptively adjusted depending on noise variance [11].…”

Section: Introductionmentioning

confidence: 99%

“…Although there are such applications as synthetic aperture radar (SAR) imaging with known number of looks and image forming mode for which speckle characteristics can be accurately predicted [10], it appears a more practical situation when noise characteristics are fully or partly unknown (unavailable). For example, for color images acquired by digital cameras, noise properties are determined by camera settings, illumination conditions, and other factors [9,[11][12][13]. Similarly, noise characteristics might be considerably different in sub-band images of multi-and hyperspectral remote sensing data acquired from airborne and space-borne platforms [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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On required accuracy of mixed noise parameter estimation for image enhancement via denoising

et al. 2014

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Characteristics of noise (type, statistics, spatial correlation) are nowadays exploited in many image denoising and enhancement methods. However, these characteristics are often unknown, and they have to be extracted from an image at hand. There are many powerful and accurate blind methods for noise variance estimation for the cases of additive and multiplicative noise models. However, more complicated noise models containing a mixture of signal-independent (SI) and signal-dependent (SD) components are often more adequate in practice. Parameters of both components have to be automatically estimated to be used in image enhancement. This paper addresses a question of required accuracy of such estimation. Analysis is carried out for color images processed by a filter based on discrete cosine transform. The influence of errors in mixed noise parameters estimation is studied in terms of filtering efficiency. This efficiency is characterized by the conventional criterion peak signal-to-noise ratio (PSNR) and two visual quality metrics, PSNR human visual system masking (PSNR-HVS-M) and multi-scale structural similarity (MSSIM). If a reduction of filtering efficiency exceeds 0.5 dB (in terms of PSNR and PSNR-HVS-M) or 0.005 (in terms of MSSIM), mixed noise parameters estimation is assumed to be unacceptable. As the result, it is shown that SI and SD noise parameters have to be estimated with a relative error not exceeding 20%…30%.

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