No-reference image quality assessment using fusion metric
| Autor: | Kulbir Singh, Jayashri V. Bagade, Yogesh H. Dandawate |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Fusion
Computer Networks and Communications Computer science business.industry Image quality Scale-invariant feature transform 020207 software engineering Pattern recognition 02 engineering and technology Scale invariance Singularity Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Media Technology Artificial intelligence business Classifier (UML) Software |
| Zdroj: | Multimedia Tools and Applications. 79:2109-2125 |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-019-08217-5 |
| Popis: | This paper presents a fusion featured metric for no-reference image quality assessment of natural images. Natural images exhibit strong statistical properties across the visual contents such as leading edge, high dimensional singularity, scale invariance, etc. The leading edge represents the strong presence of continuous points, whereas high singularity conveys about non-continuous points along the curves. Both edges and curves are equally important in perceiving the natural images. Distortions to the image affect the intensities of these points. The change in the intensities of these key points can be measured using SIFT. However, SIFT tends to ignore certain points such as the points in the low contrast region which can be identified by curvelet transform. Therefore, we propose a fusion of SIFT key points and the points identified by curvelet transform to model these changes. The proposed fused feature metric is computationally efficient and light on resources. The neruofuzzy classifier is employed to evaluate the proposed feature metric. Experimental results show a good correlation between subjective and objective scores for public datasets LIVE, TID2008, and TID2013. |
| Databáze: | OpenAIRE |
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