Abstract
Blind image quality assessment (BIQA) of authentically distorted images is a challenging problem due to the lack of a reference image and the coexistence of blends of distortions with unknown characteristics. In this article, we present a convolutional neural network based BIQA model. It encodes the input image into multi-level features to estimate the perceptual quality score. The proposed model is designed to predict the image quality score but is trained for jointly treating the image quality assessment as a classification, regression, and pairwise ranking problem. Experimental results on three different datasets of authentically distorted images show that the proposed method achieves comparable results with state-of-the-art methods in intra-dataset experiments and is more effective in cross-dataset experiments.
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Data availability
Data underlying the results presented in this paper are available in Refs. [15,20,21,66].
15. V. Hosu, H. Lin, T. Sziranyi, and D. Saupe, “KonIQ-10k: an ecologically valid database for deep learning of blind image quality assessment,” IEEE Trans. Image Process. 29, 4041–4056 (2020). [CrossRef]
20. D. Ghadiyaram and A. C. Bovik, “Massive online crowd sourced study of subjective and objective picture quality,” IEEE Trans. Image Process. 25, 372–387 (2016). [CrossRef]
21. Y. Fang, H. Zhu, Y. Zeng, K. Ma, and Z. Wang, “Perceptual quality assessment of smartphone photography,” in CVPR (IEEE, 2020), pp. 3677–3686.
66. L. Celona and R. Schettini, “Blind image quality assessment of authentically distorted images,” GitHub (2021) [accessed 8 November 2021], https://github.com/CeLuigi/BIQA4ConsumerPhotographs.
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