Quality Assessment of In-the-Wild Videos

Quality assessment of in-the-wild videos is a challenging problem because of the absence of reference videos and shooting distortions. Knowledge of the human visual system can help establish methods for objective quality assessment of in-the-wild videos. In this work, we show two eminent effects of the human visual system, namely, content-dependency and temporal-memory effects, could be used for this purpose. We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network. For content-dependency, we extract features from a pre-trained image classification neural network for its inherent content-aware property. For temporal-memory effects, long-term dependencies, especially the temporal hysteresis, are integrated into the network with a gated recurrent unit and a subjectively-inspired temporal pooling layer. To validate the performance of our method, experiments are conducted on three publicly available in-the-wild video quality assessment databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm, respectively. Experimental results demonstrate that our proposed method outperforms five state-of-the-art methods by a large margin, specifically, 12.39%, 15.71%, 15.45%, and 18.09% overall performance improvements over the second-best method VBLIINDS, in terms of SROCC, KROCC, PLCC and RMSE, respectively. Moreover, the ablation study verifies the crucial role of both the content-aware features and the modeling of temporal-memory effects. The PyTorch implementation of our method is released at https://github.com/lidq92/VSFA.Comment: 9 pages, 7 figures, 4 tables. ACM Multimedia 2019 camera ready. -> Update alignment formatting of Table

A novel objective no-reference metric for digital video quality assessment

A novel objective no-reference metric is proposed for video quality assessment of digitally coded videos containing natural scenes. Taking account of the temporal dependency between adjacent images of the videos and characteristics of the human visual system, the spatial distortion of an image is predicted using the differences between the corresponding translational regions of high spatial complexity in two adjacent images, which are weighted according to temporal activities of the video. The overall video quality is measured by pooling the spatial distortions of all images in the video. Experiments using reconstructed video sequences indicate that the objective scores obtained by the proposed metric agree well with the subjective assessment scores