TY - GEN
T1 - Image quality assessment for video surveillance system
AU - Shen, Jianhua
AU - Zhang, Hongyan
AU - Wang, Ci
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - With the popularity of surveillance system, traditional method to daily keep watch on its performance by human cannot meet the requirements anymore. Image degradation is a progressive process and its ideal version can be captured at beginning. The objects in the scene may change during its usage, so that the image content to be examined will be significantly different with the referred one. Therefore, the full-reference (FR) image quality assessments (IQAs) are no longer efficient for this application. In this paper, a reduced-reference (RR) IQA is proposed to fit the distribution of MSCN coefficients as the low level feature, and the feature is combined with the content representation. This feature is associated with MOS by SVR to produce the IQA model. We validate the performance of our method with an extensive study involving 1000 surveillance images and experimental results show that the method fits with the subjective evaluation better than the existing FR and NR algorithms.
AB - With the popularity of surveillance system, traditional method to daily keep watch on its performance by human cannot meet the requirements anymore. Image degradation is a progressive process and its ideal version can be captured at beginning. The objects in the scene may change during its usage, so that the image content to be examined will be significantly different with the referred one. Therefore, the full-reference (FR) image quality assessments (IQAs) are no longer efficient for this application. In this paper, a reduced-reference (RR) IQA is proposed to fit the distribution of MSCN coefficients as the low level feature, and the feature is combined with the content representation. This feature is associated with MOS by SVR to produce the IQA model. We validate the performance of our method with an extensive study involving 1000 surveillance images and experimental results show that the method fits with the subjective evaluation better than the existing FR and NR algorithms.
KW - Image quality assessment
KW - Mean subtracted contrast normalized
KW - Reduced-reference
KW - Support vector regression
KW - Video surveillance system
UR - https://www.scopus.com/pages/publications/85047457281
U2 - 10.1007/978-3-319-77383-4_82
DO - 10.1007/978-3-319-77383-4_82
M3 - 会议稿件
AN - SCOPUS:85047457281
SN - 9783319773827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 838
EP - 846
BT - Advances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
A2 - Zeng, Bing
A2 - Li, Hongliang
A2 - Huang, Qingming
A2 - El Saddik, Abdulmotaleb
A2 - Jiang, Shuqiang
A2 - Fan, Xiaopeng
PB - Springer Verlag
T2 - 18th Pacific-Rim Conference on Multimedia, PCM 2017
Y2 - 28 September 2017 through 29 September 2017
ER -