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Perceptual Quality Driven Frame-Rate Selection (PQD-FRS) for High-Frame-Rate Video
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- Authors
- Qin Huang, Se Yoon Jeong, Shanglin Yang, Dichen Zhang, Sudeng Hu, Hui Yong Kim, Jin Soo Choi, C.-C. Jay Kuo
- Issue Date
- 2016-09
- Citation
- IEEE Transactions on Broadcasting, v.62, no.3, pp.640-653
- ISSN
- 0018-9316
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Accurate prediction, Bit rate, Ground Truth, Highly accurate, Large screen display, Machine Learning Approach, Perceived quality, Perceptual Quality, Quality Assessment Dataset, Quality degradation, Rate dependent