Perceptual Quality Assessment for Multi-Exposure Image Fusion - 2015


Multi-exposure image fusion (MEF) is considered a good quality enhancement technique widely adopted in consumer electronics, but little work has been dedicated to the perceptual quality assessment of multi-exposure fused images. During this paper, we have a tendency to 1st build an MEF database and perform a subjective user study to evaluate the quality of pictures generated by totally different MEF algorithms. There are several helpful findings. First, considerable agreement has been observed among human subjects on the quality of MEF pictures. Second, no single state-of-the-art MEF algorithm produces the most effective quality for all test pictures. Third, the present objective quality models for general image fusion are terribly restricted in predicting perceived quality of MEF pictures. Motivated by the shortage of appropriate objective models, we tend to propose a novel objective image quality assessment (IQA) algorithm for MEF pictures based mostly on the principle of the structural similarity approach and a unique live of patch structural consistency. Our experimental results on the subjective database show that the proposed model well correlates with subjective judgments and considerably outperforms the existing IQA models for general image fusion. Finally, we tend to demonstrate the potential application of the proposed model by automatically tuning the parameters of MEF algorithms.1

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