Perceptual Information Completion-Based Siamese Omnidirectional Image Quality Assessment Network

Omnidirectional image quality assessment (OIQA) has become a research hotspot in the field of image processing. The existing OIQA metrics have the common problem of incomplete perceptual information representation, which results in performance limitation. To deal with this problem, a perceptual information completion-based Siamese (PICS) OIQA network is proposed. Specifically, the input OI is first reprojected to generate another OI with complementary perceptual information. Both the original input OI and the generated OI are simultaneously fed into the following QA network, forming the Siamese network structure. For each branch of the QA network, the multiresolution local features and the complementary global features are severally extracted by convolution operations and self-attention modules and then are hierarchically aggregated to more fully simulate the visual perception mechanism. Finally, the aggregated features of the output of two branches are further fused to measure the quality of the OI to be tested. Through extensive experiments on two public OIQA datasets and deep analysis of the experimental results, the performance advancement of the PICS method is demonstrated in the OIQA task.