A new method of Thangka image inpainting quality assessment

摘要

In order to solve the problem of Thangka image inpainting quality assessment (IIQA) and existing quality evaluation methods are not suitable for inpainting Thangka image, this paper proposes a new non reference quality evaluation method which can effectively solve this problem. Firstly, due to lack of original Thangka image, the proposed method using symmetry of Thangka images to predicted an undamaged image. Secondly, we extract harries corner in Thangka inpainting images to show the structural feature of total graph. Thirdly, demonstrate subjective evaluation score of inpainting Thangka image by caparison difference of structural feature between in painting and predicted original area. Finally, in order to compensate the lack of Thangka images in existing database, we add Generative Adversarial Nets (GANs) to generate large number of available image. Experiments show that our proposed method generates a state-of-the-art index for Thangka image inpainting quality which correlated with human vision. (C) 2018 Published by Elsevier Inc.