TY - GEN
T1 - Multi-indicator image quality assessment of smartphone camera based on human subjective behavior and perception
AU - Zhou, Yuwen
AU - Wang, Yunlu
AU - Kong, Youyong
AU - Hu, Menghan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - As smartphones are widely used in daily lives, manufacturers and customers are increasingly concerned about the performance of smartphone cameras. However, due to the unique distortion types, there are relatively few image quality assessment (IQA) methods for smartphone images. In this paper, we propose a smartphone photo quality assessment model, which scores from four quality aspects: color, texture, noise and exposure. Based on human observation behaviors of different indicators, two novel image cropping methods viz. SalGAN-crop based on saliency prediction and SSIM-crop based on structural similarity are proposed. Different features are after-wards extracted by simulating human subjective perception, and the predicted scores are finally given by AdaBoost regression analysis. Experimental results reveal that our model can provide more accurate scores than traditional methods.
AB - As smartphones are widely used in daily lives, manufacturers and customers are increasingly concerned about the performance of smartphone cameras. However, due to the unique distortion types, there are relatively few image quality assessment (IQA) methods for smartphone images. In this paper, we propose a smartphone photo quality assessment model, which scores from four quality aspects: color, texture, noise and exposure. Based on human observation behaviors of different indicators, two novel image cropping methods viz. SalGAN-crop based on saliency prediction and SSIM-crop based on structural similarity are proposed. Different features are after-wards extracted by simulating human subjective perception, and the predicted scores are finally given by AdaBoost regression analysis. Experimental results reveal that our model can provide more accurate scores than traditional methods.
KW - Consumer electronic products
KW - Image quality assessment
KW - Local region selection
KW - Visual perception modeling
UR - https://www.scopus.com/pages/publications/85091760607
U2 - 10.1109/ICMEW46912.2020.9105971
DO - 10.1109/ICMEW46912.2020.9105971
M3 - 会议稿件
AN - SCOPUS:85091760607
T3 - 2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
BT - 2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
Y2 - 6 July 2020 through 10 July 2020
ER -