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Evaluating Video Quality by Differentiating Between Spatial and Temporal Distortions

To objectively evaluate the quality of videos different stateof-the-art Image Quality Metrics (IQMs) have been used to introduce different Video Quality Metrics (VQM). While such approaches are able to evaluate the spatial quality of the frames in the video they are not able to address the temporal aspects of the video quality. In this study, we introduce a new full-reference VQM which is based on taking advantage of a Convolutional Neural Network (CNN) based IQM to evaluate the quality of the frame. Using other techniques such as visual saliency detection we are then able to differentiate between spatial and temporal distortions and use different pooling techniques to evaluate the quality of the video. Our results show that by detecting the type of distortion (spatial or temporal) affecting the video quality, the proposed VQM can evaluate the quality of the video with a higher accuracy.