Blind Quality Evaluator for Enhanced Colonoscopy Images by Integrating Local and Global Statistical Features

Yuhe Wang, Yueli Cui, Junhao Lin, Gangyi Jiang, Mei Yu, Chenli Fang, Shiqing Zhang

Abstract: In colonoscopy examinations, the dark imaging environment of the intestine may result in low-quality scene acquisition and pose challenges for subsequent disease diagnosis. Despite the development of numerous low-light image enhancement (LIE) methods to improve the quality of enhanced colonoscopy images (ECIs), various artifacts such as abnormal brightness and low contrast are still induced. To fairly compare various LIE algorithms and accurately measure the visual quality of ECIs, we develop a novel blind quality evaluator for ECIs by integrating local and global statistical features. For global features, we first extract multiscale gradient similarity features in the multiscale space domain and multi-resolution information entropy features in the discrete cosine transform (DCT) domain to capture their sharpness degradation. Second, we utilize asymmetric generalized Gaussian distribution (AGGD) models to extract fitting parameters in color-opponent components to measure color distortion in ECIs. In addition, a set of intermediate ECIs are generated by adjusting the brightness information, from which perceptual features are computed to capture global brightness variations. For local features, we utilize local binary pattern (LBP) descriptors in the gradient domain and combine them with contrast energy features and local color-aware features to capture local quality degradation. Finally, by integrating both local and global perceptual features, a regression function is employed to build a connection between quality-aware features and subjective ratings. Extensive experiments conducted on publicly available ECIQAD dataset demonstrate that our method outperforms current mainstream blind quality metrics.

External IDs:doi:10.1109/tim.2024.3481532