IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, pp. 1004-1008.
ISSN/ISBN: Not available at this time. DOI: 10.1109/ICIP.2019.8803047
Abstract: Natural scene statistics (NSS) model has received considerable attention in the image quality assessment (IQA) community due to its high sensitivity to image distortion. However, most existing NSS-based IQA methods extract features either from spatial domain or from transform domain. There is little work to simultaneously consider the features from these two domains. In this paper, a novel blind IQA method (NBIQA) based on refined NSS is proposed. The proposed NBIQA first investigates the performance of a large number of candidate features from both the spatial and transform domains. Based on the investigation, we construct a refined NSS model by selecting competitive features from existing NSS models and adding three new features. Then the refined NSS is fed into SVM tool to learn a simple regression model. Finally, the trained regression model is used to predict the scalar quality score of the image. Experimental results tested on both LIVE IQA and LIVE-C databases show that the proposed NBIQA performs better in terms of synthetic and authentic image distortion than current mainstream IQA methods. The source code is available at https://github.com/GZU-Image-Video-Lab/NBIQA.
Bibtex:
@INPROCEEDINGS{,
author={Fu-Zhao {Ou} and Yuan-Gen {Wang} and Guopu {Zhu}},
booktitle={2019 IEEE International Conference on Image Processing (ICIP)},
title={A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics},
year={2019},
volume={},
number={},
pages={1004--1008},
doi={10.1109/ICIP.2019.8803047},
ISSN={2381-8549},
month={Sep.},
}
Reference Type: Conference Paper
Subject Area(s): Image Processing