Perceptual Quality Assessment for Multi-Exposure Image Fusion

Multi-exposure image fusion (MEF) is considered an effective quality enhancement technique widely adopted in consumer electronics, but little work has been dedicated to the perceptual quality assessment of multi-exposure fused images. In this paper, we first build an MEF database and carry out a sub...

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Veröffentlicht in:IEEE transactions on image processing Jg. 24; H. 11; S. 3345 - 3356
Hauptverfasser: Ma, Kede, Kai Zeng, Zhou Wang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.11.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Computational modeling
Computer vision
Defense industry
Image edge detection
Image fusion
Image processing
Image quality
image quality assessment
Image sequences
luminance consistency
Mathematical models
Matlab
Multi-exposure image fusion (MEF)
perceptual image processing
Prediction algorithms
Quality
Quality assessment
structural similarity
subjective evaluations
structural similarity
perceptual image processing
image quality assessment
luminance consistency
subjective evaluations
Multi-exposure image fusion (MEF)
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Multi-exposure image fusion (MEF) is considered an effective quality enhancement technique widely adopted in consumer electronics, but little work has been dedicated to the perceptual quality assessment of multi-exposure fused images. In this paper, we first build an MEF database and carry out a subjective user study to evaluate the quality of images generated by different MEF algorithms. There are several useful findings. First, considerable agreement has been observed among human subjects on the quality of MEF images. Second, no single state-of-the-art MEF algorithm produces the best quality for all test images. Third, the existing objective quality models for general image fusion are very limited in predicting perceived quality of MEF images. Motivated by the lack of appropriate objective models, we propose a novel objective image quality assessment (IQA) algorithm for MEF images based on the principle of the structural similarity approach and a novel measure of patch structural consistency. Our experimental results on the subjective database show that the proposed model well correlates with subjective judgments and significantly outperforms the existing IQA models for general image fusion. Finally, we demonstrate the potential application of the proposed model by automatically tuning the parameters of MEF algorithms. 1
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2015.2442920