TVFormer: Trajectory-guided Visual Quality Assessment on 360° Images with Transformers

Abstract

Visual quality assessment (VQA) on 360° images plays an important role in optimizing immersive multimedia systems. Due to the absence of pristine 360° images in real world, blind VQA (BVQA) on 360° images has drawn much research attention. In subjective VQA on 360^ images, human intuitively make the quality-scoring decisions through the quality degradation of each observed viewport on the head trajectories. Unfortunately, the existing BVQA works for 360° images neglect the dynamic property of head trajectories with viewport interactions, thus failing to obtain human-like quality scores. In this paper, we propose a novel Transformer-based approach for trajectory-guided VQA on 360° images (named TVFormer), in which both the tasks of head trajectory prediction and BVQA can be accomplished for 360° images. In the first task, we develop a trajectory-aware memory updater (TMU) module, for maintaining the coherence and accuracy of predicted head trajectories. To capture the long-range quality dependency across time-ordered viewports, we propose a spatio-temporal factorized self-attention (STF) module in the encoder of TVFormer for the BVQA task. By implanting the predicted head trajectories into the BVQA task, we can obtain the human-like quality scores. Extensive experiments demonstrate the superior BVQA performance of TVFormer over state-of-the-art approaches on three benchmark datasets.