Perceptual Quality Assessment of Enhanced Colonoscopy Images: A Benchmark Dataset and an Objective Method

Abstract

In colonoscopy, the captured images are usually with low-quality appearance, such as non-uniform illumination, low contrast, etc., due to the specialized imaging environment, which may provide poor visual feedback and bring challenges to subsequent disease analysis. Many low-light image enhancement (LIE) algorithms have recently proposed to improve the perceptual quality. However, how to fairly evaluate the quality of enhanced colonoscopy images (ECIs) generated by different LIE algorithms remains a rarely-mentioned and challenging problem. In this study, we carry out a pioneering investigation on perceptual quality assessment of ECIs. Firstly, considering the lack of specific datasets, we collect 300 low-light images with diverse contents during the real-world colonoscopy and conduct rigorous subjective studies to compare the performance of 8 popular LIE methods, resulting in a benchmark dataset (named ECIQAD) for ECIs. Secondly, in view of the distinctive distortion characteristics of ECIs, we propose an effective no-reference Enhanced Colonoscopy Image Quality (ECIQ) method to automatically evaluate the perceptual quality of ECIs via analysis of brightness, contrast, colorfulness, naturalness, and noise. Extensive experiments on ECIQAD demonstrate the superiority of our proposed ECIQ method over 14 mainstream no-reference image quality assessment methods.