TY - JOUR
T1 - Deep activation pooling for blind image quality assessment
AU - Zhang, Zhong
AU - Wang, Hong
AU - Liu, Shuang
AU - Durrani, Tariq S.
N1 - Publisher Copyright:
© 2018 by the authors.
PY - 2018/3/21
Y1 - 2018/3/21
N2 - Driven by the rapid development of digital imaging and network technologies, the opinion-unaware blind image quality assessment (BIQA) method has become an important yet very challenging task. In this paper, we design an effective novel scheme for opinion-unaware BIQA.We first utilize the convolutional maps to select high-contrast patches, and then we utilize these selected patches of pristine images to train a pristine multivariate Gaussian (PMVG) model. In the test stage, each high-contrast patch is fitted by a test MVG (TMVG) model, and the local quality score is obtained by comparing with the PMVG. Finally, we propose the deep activation pooling (DAP) to automatically emphasize the more important scores and suppress the less important ones so as to obtain the overall image quality score. We verify the proposed method on two widely used databases, that is, the computational and subjective image quality (CSIQ) and the laboratory for image and video engineering (LIVE) databases, and the experimental results demonstrate that the proposed method achieves better results than the state-of-the-art methods.
AB - Driven by the rapid development of digital imaging and network technologies, the opinion-unaware blind image quality assessment (BIQA) method has become an important yet very challenging task. In this paper, we design an effective novel scheme for opinion-unaware BIQA.We first utilize the convolutional maps to select high-contrast patches, and then we utilize these selected patches of pristine images to train a pristine multivariate Gaussian (PMVG) model. In the test stage, each high-contrast patch is fitted by a test MVG (TMVG) model, and the local quality score is obtained by comparing with the PMVG. Finally, we propose the deep activation pooling (DAP) to automatically emphasize the more important scores and suppress the less important ones so as to obtain the overall image quality score. We verify the proposed method on two widely used databases, that is, the computational and subjective image quality (CSIQ) and the laboratory for image and video engineering (LIVE) databases, and the experimental results demonstrate that the proposed method achieves better results than the state-of-the-art methods.
KW - Deep activation pooling
KW - High-contrast patch selection
KW - Image quality assessment
UR - http://www.scopus.com/inward/record.url?scp=85044377196&partnerID=8YFLogxK
U2 - 10.3390/app8040478
DO - 10.3390/app8040478
M3 - Article
AN - SCOPUS:85044377196
SN - 2076-3417
VL - 8
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 4
M1 - 478
ER -