Abstract
The introduction of convolutional neural network (CNN) in no-reference image quality assessment (NR-IQA) gains great success in improving its prediction accuracy, and the performance of CNN relies on the magnitude of training samples. However, many widely-used existing image databases cannot provide adequate samples for CNN training. In this paper, we propose a pair-comparing based convolutional neural network (PC-CNN) for blind image quality assessment. By taking reference images into consideration, we generate more training samples of patch pairs by different combinations of distorted images and reference image. We build a new CNN network which has two inputs for patch pairs and two outputs predicting the scores of patches. We conduct extensive experiments to evaluate the performance of our proposed PC-CNN, and the results show that it outperforms many state-of-the-art methods.
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This work was supported by the National Natural Science Foundation of China (No. 61672118).
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Qin, X., Xiang, T., Yang, Y., Liao, X. (2019). Pair-Comparing Based Convolutional Neural Network for Blind Image Quality Assessment. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_45
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