Perceptual Quality Driven Frame-Rate Selection (PQD-FRS) for High-Frame-Rate Video
Video of higher frame rates (HFR) reduces the visual artifact in large screen display at the cost of a higher coding bit rate (or transmission bandwidth). In this work, we propose a perceptual quality driven frame rate selection (PQD-FRS) method that assigns a time-varying frame rate to a sequence so as to reduce its transmission cost. The objective of the PQD-FRS method is to offer perceptually indistinguishable experience for a certain percentage of viewers. We first conduct a subjective test to characterize the relationship between human perceived quality and video contents, and build a frame-rate-dependent video quality assessment dataset to serve as the ground truth. Then, we use a machine learning approach for the design of the key module of the PQD-FRS method, called the “satisfied user ratio (SUR) prediction.” The SUR prediction module predicts the percentage of satisfied viewers, who cannot differentiate video quality of a lower and HFR, using the support vector regression. It is confirmed by experimental results that the proposed SUR module can offer a so highly accurate prediction that the PQD-FRS system can dynamically assign a proper frame rate to video without any perceptual quality degradation for a majority of viewers.