Combined full-reference image visual quality metrics

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.

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Combining full-reference image visual quality metrics by neural network

10.1117/12.2085465 ◽

2015 ◽

Cited By ~ 3

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

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

Journal of Imaging ◽

10.3390/jimaging5010020 ◽

2019 ◽

Vol 5 (1) ◽

pp. 20 ◽

Cited By ~ 1

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.

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