Add to BibSonomy
Abstract
Compared with monocular images, scene discrepancies between the left- and right-view images impose additional challenges on visual quality predictions in binocular images. Herein, we propose a hierarchical feature fusion network (HFFNet) for blind binocular image quality prediction that handles scene discrepancies and uses multilevel fusion features from the left- and right-view images to reflect distortions in binocular images. Specifically, a feature extraction network based on MobileNetV2 is used to determine the feature layers from distorted binocular images; then, low-level binocular fusion features (or middle-level and high-level binocular fusion features) are obtained by fusing the left and right low-level monocular features (or middle-level and high-level monocular features) using the feature gate module; further, three feature enhancement modules are used to enrich the information of the extracted features at different levels. Finally, the total feature maps obtained from the high-, middle-, and low-level fusion features are applied to a three-input feature fusion module for feature merging. Thus, the proposed HFFNet provides better results, to the best of our knowledge, than existing methods on two benchmark datasets.
© 2022 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Yong Ding and Yang Zhao
Appl. Opt. 57(10) 2610-2621 (2018)
Wujie Zhou and Lu Yu
Appl. Opt. 54(21) 6549-6557 (2015)
Feng Shao, Kemeng Li, Gangyi Jiang, Mei Yu, and Changhong Yu
Appl. Opt. 54(33) 9671-9680 (2015)