Recent Advances and Challenges in Video Quality Assessment

doi:10.12142/ZTECOM.201901002

ZTE Communications ›› 2019, Vol. 17 ›› Issue (1): 3-11.DOI: 10.12142/ZTECOM.201901002

收稿日期:2018-06-09 出版日期:2019-02-20 发布日期:2019-11-14

Recent Advances and Challenges in Video Quality Assessment

LI Dingquan, JIANG Tingting, JIANG Ming

Peking University, Beijing 100871, China

Received:2018-06-09 Online:2019-02-20 Published:2019-11-14
About author:LI Dingquan received the double B.S. degrees in electronic science and technology & applied mathematics from Nankai University, China in 2015, and he is currently working toward the Ph.D. degree in applied mathematics at Peking University, China. He is a member of National Engineering Lab for Video Technology. His research interests include image/video quality assessment, perceptual optimization, and machine learning. He has published papers in IEEE Transactions on Multimedia and ACM Multimedia Conference.|JIANG Tingting (ttjiang@pku.edu.cn) received the B.S. degree in computer science from University of Science and Technology of China in 2001 and the Ph.D. degree in computer science from Duke University, USA in 2007. She is currently an associate professor of computer science at Peking University, China. Her research interests include computer vision and image/video quality assessment. She has published more than 40 papers in journals and conferences.|JIANG Ming received the B.Sc. and Ph.D. degrees in mathematics from Peking University, China in 1984 and 1989, respectively. He is a professor with Department of Information Science, School of Mathematical Science, Peking University since 2002. His research interests are mathematical and technical innovations in biomedical imaging and image processing.
Supported by:
National Basic Research Program of China (“973” Program)(2015CB351803);the National Natural Science Foundation of China(61390514);the National Natural Science Foundation of China(61527804);the National Natural Science Foundation of China(61572042);the National Natural Science Foundation of China(61520106004);Sino-German Center (GZ 1025)(GZ 1025);We also acknowledge the High-Performance Computing Platform of Peking University for providing computational resources.

摘要/Abstract

Abstract:

Video quality assessment (VQA) plays a vital role in the field of video processing, including areas of video acquisition, video filtering in retrieval, video compression, video restoration, and video enhancement. Since VQA has gained much attention in recent years, this paper gives an up-to-date review of VQA research and highlights current challenges in this filed. The subjective study and common VQA databases are first reviewed. Then, a survey on the objective VQA methods, including full-reference, reduced-reference, and no-reference VQA, is reported. Last but most importantly, the key limitations of current research and several challenges in the field of VQA are discussed, which include the impact of video content, memory effects, computational efficiency, personalized video quality prediction, and quality assessment of newly emerged videos.

Key words: databases, perceptual optimization, personalization, video content, VQA

. [J]. ZTE Communications, 2019, 17(1): 3-11.

LI Dingquan, JIANG Tingting, JIANG Ming. Recent Advances and Challenges in Video Quality Assessment[J]. ZTE Communications, 2019, 17(1): 3-11.

图/表 2

Table 1 VQA databases with the subjective study methods, numbers of (#) reference/distorted videos and score types

VQA Database	Subjective Study Method	#Reference/Distorted Videos	Score Type^**
VQEG FR-TV Phase I [9]	DSCQS	22/352	DMOS+σ
VQEG HDTV [10]	ACR-HR	49/740	Raw
EPFL-PoliMI [11]	SS/ACR	12/156	Raw
LIVE [12]	ACR-HR	10/150	DMOS+σ
LIVE Mobile [13]	SSCQE-HR	10/200	DMOS+σ
CSIQ [14]	SAMVIQ	12/216	DMOS+σ
CVD2014 [15]	SS/ACR	None/234	Raw
LIVE-Qualcomm [16]	SS/ACR	None/208	MOS+σ
KoNViD-1k^* [17]	SS/ACR	None/1 200	Raw
LIVE-VQC^* [18]	SS/ACR	None/585	MOS+σ

Figure 1. The six images are the first frame of six diferent distorted videos in LIVE-Qualcomm [16]. The distortions in these videos are at similar levels: a) NightSence, mean opinion socre (MOS)=35.7252; b) NightSence, MOS=32.2755; c) DogsOnBeach, MOS=65.9423; d) DogsOnBeach, MOS=61.2497; e) ManUnderTree, MOS=52.5666; f) ManUnderTree, MOS=57.2888. It can be seen that the perceived video quality does strongly depend on video content.

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