Bilinear CNNs for Blind Quality Assessment of Fine-Grained Images

Most of the existing image quality assessment (IQA) studies focus on discriminable images, whose relative visual quality could be easily determined by human beings (we also call this issue coarse-grained (CG)-IQA). The effective models designed for CG-IQA struggle for quality assessment of the images with subtle differences (often exist in many real applications), which is also called fine-grained (FG) IQA problem. Thus, we make the first, to the best of our knowledge, attempt to build a novel blind IQA (BIQA) model for the images with FG distortion, aiming to fill the gap between objective IQA model and real applications. Specifically, the proposed model mainly consists of a feature extraction module (a sequence of convolution layers), a squeeze-and-excitation module, and a bilinear pooling module, whose objectives are extracting quality-aware features, enhancing features' representation ability, and discriminability. We conduct extensive experiments on a public FG-IQA database, and demonstrate the superiority of the proposed method and the effectiveness of each module.