  Quality assessment for User Generated Content (UGC) videos plays an important
role in ensuring the viewing experience of end-users. Previous UGC video
quality assessment (VQA) studies either use the image recognition model or the
image quality assessment (IQA) models to extract frame-level features of UGC
videos for quality regression, which are regarded as the sub-optimal solutions
because of the domain shifts between these tasks and the UGC VQA task. In this
paper, we propose a very simple but effective UGC VQA model, which tries to
address this problem by training an end-to-end spatial feature extraction
network to directly learn the quality-aware spatial feature representation from
raw pixels of the video frames. We also extract the motion features to measure
the temporal-related distortions that the spatial features cannot model. The
proposed model utilizes very sparse frames to extract spatial features and
dense frames (i.e. the video chunk) with a very low spatial resolution to
extract motion features, which thereby has low computational complexity. With
the better quality-aware features, we only use the simple multilayer perception
layer (MLP) network to regress them into the chunk-level quality scores, and
then the temporal average pooling strategy is adopted to obtain the video-level
quality score. We further introduce a multi-scale quality fusion strategy to
solve the problem of VQA across different spatial resolutions, where the
multi-scale weights are obtained from the contrast sensitivity function of the
human visual system. The experimental results show that the proposed model
achieves the best performance on five popular UGC VQA databases, which
demonstrates the effectiveness of the proposed model. The code will be publicly
available.
