Open AccessObjective estimation of subjective image quality assessment using multi‐parameter prediction
Author(s) -
MaksimovićMoićević Sanja,
Lukač Željko,
Temerinac Miodrag
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6143
Subject(s) - image quality , artificial intelligence , computer science , measure (data warehouse) , image processing , pattern recognition (psychology) , standard test image , feature (linguistics) , interpolation (computer graphics) , image compression , image (mathematics) , similarity (geometry) , noise (video) , quality (philosophy) , computer vision , data mining , linguistics , philosophy , epistemology
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.