Analysis of HVS-Metrics’ Properties Using Color Image Database TID2013

Ponomarenko, Nikolay N.; Astola, Jaakko; Егиазарян, Карен

doi:10.1007/978-3-319-25903-1_53

Cited by 42 publications

(27 citation statements)

References 18 publications

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“…Smaller values of both metrics, in general, correspond to worse quality. PSNR at the level of 36 dB and PSNR-HVS-M around 41 dB can be considered as empirically found thresholds of distortion invisibility [23].…”

Section: Basic Dependencesmentioning

confidence: 99%

“…In other words, by setting some QS it is not easy to provide a desired quality of compressed images, at least, according to the metric PSNR and for large QS. For example, QS = 20 leads to invisibility of distortions for some images and their visibility for other images (note that, according to [23], distortions are, with high probability, invisible if PSNR > 36 dB).…”

Section: (A)mentioning

confidence: 99%

“…One is that an adequate metric should be chosen, and the corresponding distortion invisibility threshold must be known in advance. The standard peak signal-to-noise ratio (PSNR) is known to be not always a reliable metric from a human visual system point of view, a choice of other visual quality metrics can be a question alongside threshold selection [23].…”

Section: Introductionmentioning

confidence: 99%

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A Fast Method of Visually Lossless Compression of Dental Images

et al. 2020

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A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.

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Section: Basic Dependencesmentioning

confidence: 99%

Section: (A)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fast Method of Visually Lossless Compression of Dental Images

et al. 2020

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“…It is seen that the values of this metric are larger than 45 dB for 12 out of 13 component images. This means that these images are of high quality and noise cannot be noticed in visualized component images [37] (one example is shown in Figure 1a). Meanwhile, there is also an image in sub-band 10 for which PSNR rob inp is only 11.6 dB and, therefore, noise is visible (one example is given in Figure 1b).…”

Section: Image/noise Model and Basic Principles Of Image Denoising Wimentioning

confidence: 99%

Enhancement of Component Images of Multispectral Data by Denoising with Reference

et al. 2019

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Multispectral remote sensing data may contain component images that are heavily corrupted by noise and the pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images—component images having relatively high quality and that are similar to the image subject to pre-filtering. Here, we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. To provide high efficiency of denoising, reference images have to be transformed using linear or nonlinear transformations. This paper proposes a practical approach to doing this. Examples of denoising tests and real-life images demonstrate high efficiency of the proposed approach.

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“…For this model, one can use the so-called equivalent noise variance that is equal to [37] (one example is shown in Figure 1,a. Meanwhile, there is also an image in sub-band 10 for which rob inp PSNR is only 11.6 dB and, thus, noise is visible (one example is given in Figure 1,b.…”

Section: Image/noise Model and Basic Principles Of Image Denoising Wimentioning

confidence: 99%

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Abramov¹,

Uss²,

Vozel³

et al. 2019

Preprint

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Multispectral remote sensing data may contain component images which are heavily corrupted by noise and pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images – component images having relatively high quality and which are similar to the image subject to pre-filtering. Here we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. Examples of denoising of real-life images demonstrate high efficiency of the proposed approach.

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Analysis of HVS-Metrics’ Properties Using Color Image Database TID2013

Cited by 42 publications

References 18 publications

A Fast Method of Visually Lossless Compression of Dental Images

A Fast Method of Visually Lossless Compression of Dental Images

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Enhancement of Component Images of Multispectral Data by Denoising with Reference

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