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Convolutional neural network with uncertainty estimates for no-reference image quality assessment
Author(s): Yuge Huang; Xiang Tian; Rongxin Jiang; Yaowu Chen
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Paper Abstract

The use of convolutional neural networks (CNNs) for general no-reference image quality assessment (NR-IQA) has seen tremendous growth in the research community. Most these methods used the patches cropped from the original images for training. For these patch-based methods, the ‘ground truth’ quality of patches is essential. In practice, these methods often took the quality score of an original image directly as the labels of its patches’ quality. However, the perceptual quality of image patches generally differs from the corresponding image quality. Thus, the noise in patches’ labels may hinder effective training of the CNN. In this paper, we propose a CNN with two branches for general noreference image quality assessment. One branch of this model predicts the patch quality, and the other predicts the uncertainty, which denotes the degree of deviation of the patch quality from the image quality. Our model can be trained in an end-to-end manner by minimizing a joint loss. We tested our model on widely used image quality databases and showed that it performed better or comparable with those of state-of-the-art NR-IQA algorithms.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691D (6 May 2019); doi: 10.1117/12.2524149
Show Author Affiliations
Yuge Huang, Zhejiang Univ. (China)
Xiang Tian, Zhejiang Univ. (China)
Rongxin Jiang, Zhejiang Univ. (China)
Yaowu Chen, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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