  In this paper, we propose a deep learning based video quality assessment
(VQA) framework to evaluate the quality of the compressed user's generated
content (UGC) videos. The proposed VQA framework consists of three modules, the
feature extraction module, the quality regression module, and the quality
pooling module. For the feature extraction module, we fuse the features from
intermediate layers of the convolutional neural network (CNN) network into
final quality-aware feature representation, which enables the model to make
full use of visual information from low-level to high-level. Specifically, the
structure and texture similarities of feature maps extracted from all
intermediate layers are calculated as the feature representation for the full
reference (FR) VQA model, and the global mean and standard deviation of the
final feature maps fused by intermediate feature maps are calculated as the
feature representation for the no reference (NR) VQA model. For the quality
regression module, we use the fully connected (FC) layer to regress the
quality-aware features into frame-level scores. Finally, a
subjectively-inspired temporal pooling strategy is adopted to pool frame-level
scores into the video-level score. The proposed model achieves the best
performance among the state-of-the-art FR and NR VQA models on the Compressed
UGC VQA database and also achieves pretty good performance on the in-the-wild
UGC VQA databases.
