NDLI: No-reference quality assessment for live broadcasting videos in temporal and spatial domains

Content Provider	IET Digital Library
Author	Huang, Yipo Li, Leida Zhou, Yu Hu, Bo
Abstract	Nowadays, live broadcasting video has become increasingly popular and high-quality live broadcasting video is highly needed. In practice, live broadcasting videos usually undergo several processing stages, which inevitably introduce multiple distortions, e. g. frame freezing and intensity mutation, causing the degraded quality of experience. However, little work has been done to the quality evaluation of live broadcasting videos, which may hinder the further development of more advanced live broadcasting video delivery systems. Motivated by this, this study presents a no-reference quality evaluation model for live broadcasting videos (LBVQA) in temporal and spatial domains. In the temporal domain, statistic features are extracted to measure the frame freezing and intensity mutation, and the entropy-based feature is extracted to describe the global jitter. In the spatial domain, blurring is measured based on phase coherence, and abnormal exposure ratio is calculated based on an adaptive threshold. Finally, all features are fed into a backpropagation neural network to train the quality prediction model. Experimental results on the Live Broadcasting Video Database demonstrate the advantages of the proposed metric over the state-of-the-art image and video quality metrics.
Starting Page	774
Ending Page	781
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 4, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1195
Journal	IET Image Processing
Publisher Date	2019-11-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Abnormal Exposure Ratio Adaptive Threshold Advanced Live Broadcasting Video Delivery System Backpropagation Backpropagation Neural Network Computer Vision And Image Processing Technique Entropy Entropy-based Feature Extraction Feature Extraction Frame Freezing Global Jitter Image Sequence Intensity Mutation Live Broadcasting Video Database Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Phase Coherence Quality Prediction Model Spatial Domain Temporal Domain Video Database Video Signal Processing Video Streaming
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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