Abstract
The reduced-reference video quality evaluation method uses only partial reference video information to evaluate the quality of deteriorated videos. This method can evaluate the quality of a video in real-time because less transmission bandwidth is required. Because the video active area attracts significant human eye attention, any deterioration in the active area will directly affect video evaluation results. Given the advantage of reduced reference model in VQM (Video Qualify Metric), this paper proposes a reduced reference evaluation model named RR-PEVQ (Reduced Reference Perceptual Evaluation of Video Quality) for weighting the active video area. According to the experimental results, the RR-PEVQ evaluation score is similar to that of the full reference PEVQ and the proposed method’s practicability is greatly improved for big data purposes.
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Acknowledgments
The work presented in this paper was partially supported by 2011 National Natural Science Foundation of China (Grant number 61172097), 2012 Natural Science Foundation of Fujian (Grant number 2012J01424), NCETFJ, Fundamental Research Funds for the Central Universities (2012121028), and NSFC (61271242,61001072), Natural Science Foundation of Fujian Province of China (No.2010J01347), SRF for ROCS, SEM.
This research is supported by the Comba fund.
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Xu, WJ., Zhao, CD., Chiang, HP. et al. The RR-PEVQ algorithm research based on active area detection for big data applications. Multimed Tools Appl 74, 3507–3520 (2015). https://doi.org/10.1007/s11042-014-1903-8
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DOI: https://doi.org/10.1007/s11042-014-1903-8