Analysis of current video databases for quality assessment

Abstract

The popularity of video streaming has grown significantly over the past few years. Video quality prediction metrics can be used to perform extensive video codec analysis and customize high-quality assurance. Video databases with subjective ratings form an important basis for training video quality metrics, and codecs based on machine learning algorithms. More than three dozen subjective video databases are now available. In this article, modern video databases are presented, analyzed current database and findings methods for improving. For analysis, performance criteria are proposed based on subjective assessments when creating a database of video sequences. At this stage of development, subjective assessments are the most difficult part of creating a database of video sequences, since these assessments are expensive and time-consuming. In addition, subjective experimentation is further complicated by many factors, including viewing distance, a display device, lighting conditions, vision, and mood of the subjects. This information will allow researchers to have a more detailed understanding of the video databases, a new method for collecting subjective data, and can also help in planning future experiments.