Spatiotemporal Video Quality Assessment Method via Multiple Feature Mappings

Progressed video quality assessment (VQA) methods aim to evaluate the perceptual quality of videos in many applications but often prompt to increase computational complexity. Problems derive from the complexity of the distorted videos that are of significant concern in the communication industry, as well as the spatial-temporal content of the two-fold (spatial and temporal) distortion. Therefore, the findings of the study indicate that the information in the spatiotemporal slice (STS) images are useful in measuring video distortion. This paper mainly focuses on developing on a full reference video quality assessment algorithm estimator that integrates several features of spatiotemporal slices (STSS) of frames to form a high-performance video quality. This research work aims to evaluate video quality by utilizing several VQA databases by the following steps: (1) we first arrange the reference and test video sequences into a spatiotemporal slice representation. A collection of spatiotemporal feature maps were computed on each reference-test video. These response features are then processed by using a Structural Similarity (SSIM) to form a local frame quality.  (2) To further enhance the quality assessment, we combine the spatial feature maps with the spatiotemporal feature maps and propose the VQA model, named multiple map similarity feature deviation (MMSFD-STS). (3) We apply a sequential pooling strategy to assemble the quality indices of frames in the video quality scoring. (4) Extensive evaluations on video quality databases show that the proposed VQA algorithm achieves better/competitive performance as compared with other state- of- the- art methods

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