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

VMQ: an algorithm for measuring the Video Motion Quality

This paper proposes a new full-reference algorithm, called Video Motion Quality (VMQ) that evaluates the relative motion quality of the distorted video generated from the reference video based on all the frames from both videos. VMQ uses any frame-based metric to compare frames from the original and distorted videos. It uses the time stamp for each frame to measure the intersection values. VMQ combines the comparison values with the intersection values in an aggregation function to produce the final result. To explore the efficiency of the VMQ, we used a set of raw, uncompressed videos to generate a new set of encoded videos. These encoded videos are then used to generate a new set of distorted videos which have the same video bit rate and frame size but with reduced frame rate. To evaluate the VMQ, we applied the VMQ by comparing the encoded videos with the distorted videos and recorded the results. The initial evaluation results showed compatible trends with most of subjective evaluation results