Image Quality Assessment of Multi-exposure Image Fusion for Both Static and Dynamic Scenes

Abstract

Over the past decade, many multi-exposure image fusion (MEF) methods have been proposed to obtain perceptually appealing results for both static and dynamic scenes. However, little work has been dedicated to evaluate perceptual quality of fused images. In this work, we propose a novel objective image quality assessment (IQA) model for MEF images of both static and dynamic scenes based on a pyramid subband contrast preservation scheme and an information theory adaptive pooling strategy. Firstly, we decompose the images using a Laplacian pyramid, and each pyramid subband is used to extract gradient and contrast features. Secondly, we binarize the structure inconsistency map between each exposure and fused image to obtain large-changed and small-changed regions. Finally, an information theory adaptive pooling strategy is used to combine these two quality scores from the individual regions. Experimental results on two large scale MEF databases of static and dynamic sequences show that the proposed model can obtain superior performance than state-of-the-art models designed for fused images.