Cross-Projection Distilling Knowledge for Omnidirectional Image Quality Assessment

Nowadays, virtual reality technology is advancing rapidly and becoming increasingly matured. Omnidirectional images have integrated into the daily lives of many individuals. However, these images are susceptible to irreversible distortion during the encoding and transmission processes. Given the unique characteristics of deformation and distortion in omnidirectional images, the development of a quality assessment method is crucial. To ensure that our network not only delivers efficient and stable performance but also maintains a minimal parameter count, we have integrated the concept of knowledge distillation into our network. This involves utilizing a full-reference (FR) teacher network to guide the training of a no-reference (NR) student network by cross-projection distilling knowledge. To specifically implement this method, a Dual Projection Format Fusion (DPFF) module is specifically designed to complement and integrate the mutual fusion of the two projection formats of omnidirectional images. In the design of our knowledge distillation process and loss function, we have introduced a review mechanism to enhance the performance and efficiency of response-based knowledge, as well as utilized intermediate fusion features to improve the effectiveness of feature-based knowledge. These components are combined to formulate the final loss function. Experimental results validate the superiority of our proposed model over existing FR and NR methods when evaluated on four omnidirectional image databases. This highlights the effectiveness of our proposed model in elevating the quality assessment of omnidirectional images.