TY - GEN
T1 - COME for No-Reference Video Quality Assessment
AU - Wang, Chunfeng
AU - Su, Li
AU - Zhang, Weigang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - Nowadays, the issue of objective Video Quality Assessment (VQA) has been extensively studied. In this paper, we present an effective general-purpose VQA method named COnvolutional neural network and Multi-regression based Evaluation (COME). It requires no referred lossless video and is universal for non-specific types of distortion. A modified 2D convolutional neural network is introduced to learn the spatial features at frame level. At the same time, the motion information is extracted as temporal features at sequence level. And a multi-regression model is proposed to comprehensively assess the final video quality according to human's psychological perception. The proposed method is tested on two commonly used databases with numerous kinds of distortions. The experimental results show that the proposed COME method is comparable with most popular full-reference VQA methods.
AB - Nowadays, the issue of objective Video Quality Assessment (VQA) has been extensively studied. In this paper, we present an effective general-purpose VQA method named COnvolutional neural network and Multi-regression based Evaluation (COME). It requires no referred lossless video and is universal for non-specific types of distortion. A modified 2D convolutional neural network is introduced to learn the spatial features at frame level. At the same time, the motion information is extracted as temporal features at sequence level. And a multi-regression model is proposed to comprehensively assess the final video quality according to human's psychological perception. The proposed method is tested on two commonly used databases with numerous kinds of distortions. The experimental results show that the proposed COME method is comparable with most popular full-reference VQA methods.
KW - 2D convolutional neural network
KW - AlextNet
KW - multi regression model
KW - spatial and temporal feature
KW - video quality assessment
UR - https://www.scopus.com/pages/publications/85050155824
U2 - 10.1109/MIPR.2018.00056
DO - 10.1109/MIPR.2018.00056
M3 - 会议稿件
AN - SCOPUS:85050155824
T3 - Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
SP - 232
EP - 237
BT - Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018
Y2 - 10 April 2018 through 12 April 2018
ER -