Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video
  Quality Assessment
Hongbo Liu Mingda Wu Kun Yuan Ming Sun Yansong Tang Chuanchuan Zheng Xing Wen Xiu Li
Video quality assessment (VQA) has attracted growing attention in recent
years. While the great expense of annotating large-scale VQA datasets has
become the main obstacle for current deep-learning methods. To surmount the
constraint of insufficient training data, in this paper, we first consider the
complete range of video distribution diversity (\ie content, distortion,
motion) and employ diverse pretrained models (\eg architecture, pretext task,
pre-training dataset) to benefit quality representation. An Adaptive Diverse
Quality-aware feature Acquisition (Ada-DQA) framework is proposed to capture
desired quality-related features generated by these frozen pretrained models.
By leveraging the Quality-aware Acquisition Module (QAM), the framework is able
to extract more essential and relevant features to represent quality. Finally,
the learned quality representation is utilized as supplementary supervisory
information, along with the supervision of the labeled quality score, to guide
the training of a relatively lightweight VQA model in a knowledge distillation
manner, which largely reduces the computational cost during inference.
Experimental results on three mainstream no-reference VQA benchmarks clearly
show the superior performance of Ada-DQA in comparison with current
state-of-the-art approaches without using extra training data of VQA.