Feature‐based no‐reference video quality assessment using Extra Trees

Abstract

Abstract With the emergence of social networks and improvements in the internet speed, the video data has become an ever‐increasing portion of the global internet traffic. Besides the content, the quality of a video sequence is an important issue at the user end which is often affected by various factors such as compression. Therefore, monitoring the quality is crucial for the video content and service providers. A simple monitoring approach is to compare the raw video content (uncompressed) with the received data at the receiver. In most practical scenarios, however, the reference video sequence is not available. Consequently, it is desirable to have a general reference‐less method for assessing the perceived quality of any given video sequence. In this paper, a no‐reference video quality assessment technique based on video features is proposed. In particular, a long list of video features (21 sets of features, each consisting of 1 to 216 features) is considered and all possible combinations (221−1${2^{21}-1}$) for training an Extra Trees regressor is examined. This choice of the regressor is wisely selected and is observed to perform better than other common regressors. The results reveal that the top 20 performing feature subsets all outperform the existing feature‐based assessment methods in terms of the Pearson linear correlation coefficient (PLCC) or the Spearman rank order correlation coefficient (SROCC). Specially, the best performing regressor achieves PLCC=0.786${ ext{ PLCC}=0.786}$ on the test data over the KonVid‐1k dataset. It is believed that the results of the comprehensive comparison could be potentially useful for other feature‐based video‐related problems. The source codes of the implementations are publicly available.