Evaluating Multiexposure Fusion Using Image Information

Abstract

Multiexposure fusion (MEF) refers to image fusion methods that capture high dynamic range natural scenes from a set of low dynamic range camera images. In this letter, we study the problem of designing quality assessment (QA) algorithms to estimate the perceptual quality of images generated by different MEF algorithms. We develop our quality index by evaluating individual quality maps between a given fused test image and individual over-/underexposed images at multiple scales and orientations and then combining these maps across the over-/underexposed images. Our approach works on the premise that the true undistorted reference is contained across the over-/underexposed source images. We identify this true reference based on the notion of perceived image information using natural scene statistical models. It is shown that our approach outperforms the state of the art QA algorithms in terms of correlation with human perception of quality on a publicly available MEF database.