Paper Info
Title
Objective estimation of subjective image quality assessment using multi-parameter prediction
Abstract
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.
Year
DOI
Venue
2019
10.1049/iet-ipr.2018.6143
IET Image Processing
Keywords
Field
DocType
feature extraction,image fusion,image coding,image processing,data compression,mean square error methods,visual databases,distortion
Computer vision,Pattern recognition,Image quality,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
13
13
1751-9659
Citations 
PageRank 
References 
1
0.35
0
Authors
3
Name
Order
Citations
PageRank
Sanja Maksimović-Moićević110.35
zeljko lukac211.37
Miodrag Temerinac3187.42