Abstract
The image quality assessment (IQA) for generated images main focuses on the quality of perceptual aspects. Existing methods for evaluating generated images consider the overall image distribution characteristics. At present, there is no practical method for a single generated image. To address this issue, this paper proposes a solution base on the deep feature compatibility (DFC), which first collects suitable comparison images by a collection model. Then it provides an individual score by computing the compatibility of target and pictures with good perceptual quality. This method makes up for the deficiency of Inception Score (IS) in a small number of results or/and a single image. The experiment on Caltech UCSD birds 200 (CUB) shows that our method performs well on the assessment mission for generated images. Finally, we analyze the various problems of the representative IQA methods in evaluating.
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This research is supported by Sichuan Provincial Science and Technology Program (No. 2020YFS0307).
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Zhang, X., Zhang, Y., Zhang, Z., Yu, W., Jiang, N., He, G. (2020). Deep Feature Compatibility for Generated Images Quality Assessment. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_40
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DOI: https://doi.org/10.1007/978-3-030-63820-7_40
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