Convolutional neural network with uncertainty estimates for no-reference image quality assessment

Yuge Huang; Xiang Tian; Rongxin Jiang; Yaowu Chen

doi:10.1117/12.2524149

6 May 2019 Convolutional neural network with uncertainty estimates for no-reference image quality assessment

Yuge Huang, Xiang Tian, Rongxin Jiang, Yaowu Chen

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110691D (2019) https://doi.org/10.1117/12.2524149
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

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.

Citation Download Citation

Yuge Huang, Xiang Tian, Rongxin Jiang, and Yaowu Chen "Convolutional neural network with uncertainty estimates for no-reference image quality assessment", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691D (6 May 2019); https://doi.org/10.1117/12.2524149

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