No-reference quality assessment for contrast-altered images using an end-to-end deep framework

Shiyong Hu; Jia Yan; Weixia Zhang; Dexiang Deng

doi:10.1117/1.JEI.28.1.013041

19 February 2019 No-reference quality assessment for contrast-altered images using an end-to-end deep framework

Shiyong Hu, Jia Yan, Weixia Zhang, Dexiang Deng

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 1, 013041 (February 2019). https://doi.org/10.1117/1.JEI.28.1.013041

Abstract

No-reference image quality assessment (NR-IQA) aims to predict image quality consistently with subjective scores with no prior knowledge of reference images. However, contrast distortion, which is an uncommon distortion, has been largely overlooked. To address this issue, we explore the NR-IQA metric by predicting the quality of contrast-altered images, using deep-learning techniques. We adopt a two-stage training strategy due to a gap between the deep learning’s sample requirements and the insufficiency of samples in the IQA domain. A deep convolutional neural network (CNN) is first designed and is pretrained to the classification task with the help of an additional synthetic contrast-distorted dataset. Then, the pretrained CNN is fine-tuned on the target IQA dataset using an end-to-end training approach. An effective pooling method is employed to map the image representation into a subjective quality score during the fine-tuning stage. Experimental results on five public IQA databases containing contrast-altered images show that the proposed method achieves competitive results and has good generalization ability compared to other NR-IQA methods.

Citation Download Citation

Shiyong Hu, Jia Yan, Weixia Zhang, and Dexiang Deng "No-reference quality assessment for contrast-altered images using an end-to-end deep framework," Journal of Electronic Imaging 28(1), 013041 (19 February 2019). https://doi.org/10.1117/1.JEI.28.1.013041

Received: 19 September 2018; Accepted: 24 January 2019; Published: 19 February 2019

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