Lossy compression of images without visible distortions and its application

Zriakhov, Mikhail; Ponomarenko, Nikolay N.; Krivenko, Sergey; Zhang, Miao

doi:10.1109/icosp.2010.5655751

Cited by 35 publications

(13 citation statements)

References 10 publications

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“…Here, we would like to draw readers' attention to recently obtained results [59]. Visibility of distortions has been analyzed for images compressed in a lossy manner.…”

Section: Filter Efficiency Analysis For the Tid2008 Color Images Awgmentioning

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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“…Here, we would like to draw readers' attention to recently obtained results [59]. Visibility of distortions has been analyzed for images compressed in a lossy manner.…”

Section: Filter Efficiency Analysis For the Tid2008 Color Images Awgmentioning

confidence: 99%

“…Similar to PSNR, PSNR-HVS-M [59]. Also note that dynamic range D of image representation should be used in (2) and (3) instead of 255 if images are not in 8-bit representation.…”

Section: Filter Efficiency Analysis For the Tid2008 Color Images Awgmentioning

confidence: 99%

Efficiency analysis of color image filtering

Fevralev

Ponomarenko

Lukin

et al. 2011

EURASIP J. Adv. Signal Process.

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show abstract

“…The image whose MSSIM value is not less than 0.99 cannot be considered, being noticed as distinct from the original image. More details about the perceptual threshold for the MSSIM metric are shown in [25] and [26]. When M 0 is substituted into the fitting equations shown in Figure 2, the ranges of gamma are obtained, which results in low image distortion.…”

Section: Figure 2 the Mssim Of Images Varies Withmentioning

confidence: 99%

Low Image Distortion Constrained Power Saving for OLED Displays

Duan¹,

Guo²,

Shen³

et al. 2015

IJSIP

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“…The metric values vary from 0 (extremely bad quality) to 1 (perfect or ideal quality). As it is seen, this metric has another range of variation where the value 0.99 corresponds to practical invisibility of distortions if they are spread over entire image [42]. While PSNR-HVS-M exploits discrete cosine transform in blocks for its calculation, MSSIM is based on wavelets.…”

Section: Denoising Methods and Quantitative Criteriamentioning

confidence: 99%

“…Similarly to the conventional PSNR, the metric PSNR-HVS-M is expressed in decibels and larger values correspond to better visual quality. If PSNR-HVS-M exceeds 40 dB, distortions can be hardly noticed [42].…”

Section: Denoising Methods and Quantitative Criteriamentioning

confidence: 99%

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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Lossy compression of images without visible distortions and its application

Cited by 35 publications

References 10 publications

Efficiency analysis of color image filtering

Efficiency analysis of color image filtering

Low Image Distortion Constrained Power Saving for OLED Displays

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

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