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access icon free Objective estimation of subjective image quality assessment using multi-parameter prediction

© The Institution of Engineering and Technology
Received 04/09/2018, Accepted 01/07/2019, Revised 13/06/2019, Published 09/07/2019

Objective evaluation of a subjective image quality assessment plays a significant role in the various image processing applications, such as compression, interpolation and noise reduction. The subjective image quality assessment does not only depend on some objective measurable artefacts, but also on image content and kind of distortions. Thus, a multi-parameter prediction of the objective image quality assessment is proposed in this study. The prediction parameters are found minimising the mean square error related to the known subjective image quality measure (DMOS). This approach includes mostly used image quality metrics (peak signal-to-noise ratio, multi-scale structural similarity image measure, feature similarity image measure, video quality measure) and two-dimensional image quality metrics (2D IQM). The proposed multi-parameter prediction has been verified on the test image database (LIVE) for compression, noise and blur distortions with available subjective image quality measures (DMOS). More reliable estimations are obtained using multi-parameter prediction instead of only one measure. The best results are reached when an image content indicator is combined with the 2D IQM measure separately for different kinds of distortions.

Inspec keywords: image fusion; data compression; image processing; image coding; distortion; feature extraction; mean square error methods; visual databases

Other keywords: image content indicator; test image database; image processing applications; multiparameter prediction; objective image quality assessment; two-dimensional image quality metrics; video quality measure; subjective image quality assessment; objective measurable artefacts; known subjective image quality measure; feature similarity image measure; available subjective image quality measures; multiscale structural similarity image measure

Subjects: Image and video coding; Computer vision and image processing techniques; Optical, image and video signal processing; Spatial and pictorial databases; Interpolation and function approximation (numerical analysis); Other topics in statistics

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