An expandable image database for evaluation of full-reference image visual quality metrics

2020 ◽  
Vol 2020 (10) ◽  
pp. 137-1-137-6 ◽  
Author(s):  
Mykola Ponomarenko ◽  
Oleg Ieremeiev ◽  
Vladimir Lukin ◽  
Karen Egiazarian
Keyword(s):  
Gaussian Noise ◽  
Traditional Approach ◽  
Rank Order ◽  
Correlation Coefficients ◽  
Visual Quality ◽  
Image Database ◽  
Quality Metrics ◽  
Reference Image ◽  
Full Reference ◽  
Opinion Score

Traditional approach to collect mean opinion score (MOS) values for evaluation of full-reference image quality metrics has two serious drawbacks. The first drawback is a nonlinearity of MOS, only partially compensated by the use of rank order correlation coefficients in a further analysis. The second drawback are limitations on number of distortion types and distortion levels in image database imposed by a maximum allowed time to carry out an experiment. One of the largest of databases used for this purpose, TID2013, has almost reached these limitations, which makes an extension of TID2013 within the boundaries of this approach to be practically unfeasible. In this paper, a novel methodology to collect MOS values, with a possibility to infinitely increase a size of a database by adding new types of distortions, is proposed. For the proposed methodology, MOS values are collected for pairs of distortions, one of them being a signal dependent Gaussian noise. A technique of effective linearization and normalization of MOS is described. Extensive experiments for linearization of MOS values to extend TID2013 database are carried out.

Combined full-reference image visual quality metrics

2016 ◽  
Vol 2016 (15) ◽  
pp. 1-10 ◽  
Author(s):  
Oleg I Ieremeiev ◽  
Vladimir V Lukin ◽  
Nikolay N Ponomarenko ◽  
Karen O Egiazarian ◽  
Jaakko Astola
Keyword(s):  
Visual Quality ◽  
Quality Metrics ◽  
Reference Image ◽  
Full Reference

Combining full-reference image visual quality metrics by neural network

2015 ◽  
Author(s):  
Vladimir V. Lukin ◽  
Nikolay N. Ponomarenko ◽  
Oleg I. Ieremeiev ◽  
Karen O. Egiazarian ◽  
Jaakko Astola
Keyword(s):  
Neural Network ◽  
Visual Quality ◽  
Quality Metrics ◽  
Reference Image ◽  
Full Reference

scholarly journals Local Indicators of Spatial Autocorrelation (LISA): Application to Blind Noise-Based Perceptual Quality Metric Index for Magnetic Resonance Images

2019 ◽  
Vol 5 (1) ◽  
pp. 20 ◽  
Author(s):  
Michael Osadebey ◽  
Marius Pedersen ◽  
Douglas Arnold ◽  
Katrina Wendel-Mitoraj
Keyword(s):  
Performance Evaluation ◽  
Spatial Autocorrelation ◽  
Quality Evaluation ◽  
Magnetic Resonance Images ◽  
Quality Metrics ◽  
Quality Score ◽  
Perceptual Quality ◽  
Reference Image ◽  
Full Reference ◽  
Reference Methods

Noise-based quality evaluation of MRI images is highly desired in noise-dominant environments. Current noise-based MRI quality evaluation methods have drawbacks which limit their effective performance. Traditional full-reference methods such as SNR and most of the model-based techniques cannot provide perceptual quality metrics required for accurate diagnosis, treatment and monitoring of diseases. Although techniques based on the Moran coefficients are perceptual quality metrics, they are full-reference methods and will be ineffective in applications where the reference image is not available. Furthermore, the predicted quality scores are difficult to interpret because their quality indices are not standardized. In this paper, we propose a new no-reference perceptual quality evaluation method for grayscale images such as MRI images. Our approach is formulated to mimic how humans perceive an image. It transforms noise level into a standardized perceptual quality score. Global Moran statistics is combined with local indicators of spatial autocorrelation in the form of local Moran statistics. Quality score is predicted from perceptually weighted combination of clustered and random pixels. Performance evaluation, comparative performance evaluation and validation by human observers, shows that the proposed method will be a useful tool in the evaluation of retrospectively acquired MRI images and the evaluation of noise reduction algorithms.

scholarly journals Використання візуальних метрик для аналізу стиснення з втратами зашумлених зображень

2021 ◽  
pp. 83-91
Author(s):  
Богдан Віталійович Коваленко ◽  
Володимир Васильович Лукін
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Visual Image ◽  
Visual Quality ◽  
Low Complexity ◽  
Quality Metrics ◽  
Operating Point ◽  
Noise Variance

The subject of the article is to analyze the effectiveness of lossy image compression using a BPG encoder using visual metrics as a quality criterion. The aim is to confirm the existence of an operating point for images of varying complexity for visual quality metrics. The objectives of the paper are the following: to analyze for a set of images of varying complexity, where images are distorted by additive white Gaussian noise with different variance values, build and analyze dependencies for visual image quality metrics, provide recommendations on the choice of parameters for compression in the vicinity of the operating point. The methods used are the following: methods of mathematical statistics; methods of digital image processing. The following results were obtained. Dependencies of visual quality metrics for images of various degrees of complexity affected by noise with variance equal to 64, 100, and 196. It can be seen from the constructed dependence that a working point is present for images of medium and low complexity for both the PSNR-HVS-M and MS-SSIM metrics. Recommendations are given for choosing a parameter for compression based on the obtained dependencies. Conclusions. Scientific novelty of the obtained results is the following: for a new compression method using Better Portable Graphics (BPG), research has been conducted and the existence of an operating point for visual quality metrics has been proven, previously such studies were conducted only for the PSNR metric.The test images were distorted by additive white Gaussian noise and then compressed using the methods implemented in the BPG encoder. The images were compressed with different values of the Q parameter, which made it possible to estimate the image compression quality at different values of compression ratio. The resulting data made it possible to visualize the dependence of the visual image quality metric on the Q parameter. Based on the obtained dependencies, it can be concluded that the operating point is present both for the PSNR-HVS-M metric and for the MS-SSIM for images of medium and low complexity, it is also worth noting that, especially clearly, the operating point is noticeable at large noise variance values. As a recommendation, a formula is presented for calculating the value of the compression control parameter (for the case with the BPG encoder, it is the Q parameter) for images distorted by noise with variance varying within a wide range, on the assumption that the noise variance is a priori known or estimated with high accuracy.

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