No-Reference Quality Assessment of Infrared Image Colorization with Color–Spatial Features

LDANet represents an innovative no-reference quality assessment model specifically engineered to evaluate colorized infrared images. This is a crucial task for various applications, and existing methods often fail to capture color-specific distortions. The proposed model distinguishes itself by uniquely combining color feature extraction through latent Dirichlet allocation (LDA) with spatial feature extraction enhanced by multichannel and spatial attention mechanisms. It employs a dual-feature approach that facilitates thorough assessment of both color fidelity and detail preservation in colorized images. The architecture of LDANet encompasses two critical components: an LDA-based color feature extraction module which meticulously analyzes and learns color distribution patterns, and a spatial feature extraction module that leverages an inception network bolstered by attention mechanisms to effectively capture multiscale spatial characteristics. Rigorous experimental validation conducted on a specialized dataset of colorized infrared images demonstrates that LDANet significantly outperforms existing leading no-reference image quality assessment methods. This study reports the effectiveness of integrating color-specific features within a quality assessment framework tailored for infrared image colorization, representing a meaningful advancement in this domain. These findings emphasize the essential role of color feature integration in the evaluation of colorized infrared images, providing a robust tool for optimizing colorization algorithms and enhancing their practical applications.