상세정보
학술지
Perceptual Quality Driven Frame-Rate Selection (PQD-FRS) for High-Frame-Rate Video
- 발행일
- 201609
- 출처
- IEEE Transactions on Broadcasting, v.62 no.3, pp.640-653
- ISSN
- 0018-9316
- 출판사
- IEEE
- 협약과제
- 15MR3100, 클라우드 기반 대용량 실감미디어 제작 기술 개발, 최진수
- 초록
- 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 제안 키워드
- Accurate prediction, Bit Rate, Frame rate, Highly accurate, Large screen display, Machine Learning Approach, Perceived quality, Perceptual Quality, Quality Assessment Dataset, Quality assessment(IQA), Quality degradation