Research on the evaluation of ink animation films based on ChipGAN

Nan Liu; Hongjuan Wang; Yanmei Gao; Haonan Jin

doi:10.1117/12.3005040

20 October 2023 Research on the evaluation of ink animation films based on ChipGAN

Nan Liu, Hongjuan Wang, Yanmei Gao, Haonan Jin

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 129161Q (2023) https://doi.org/10.1117/12.3005040
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

Ink painting is a typical representative of Chinese painting. With the acceleration of globalization and the continuous development of deep learning and other technologies, Chinese ink painting urgently needs to develop into digital development. Generated adversarial network has been widely used in the field of image style transfer, among which, ChipGAN model is specifically aimed at the study of Chinese ink painting style transfer. In previous studies, we have tried to stylish the animated film ink painting, but there is no transfer learning under the ChipGAN model, and the comprehensive transfer effect evaluation is lacking. This paper will be for the animated film "big fish haitang", select five different ink style style data set, using ChipGAN model for ink style transfer, and use the SSIM image quality evaluation index and HSV color difference value from the content and style of transfer image for a more comprehensive evaluation, to explore the suitable for Chinese ink painting image transfer evaluation method lay the foundation.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nan Liu, Hongjuan Wang, Yanmei Gao, and Haonan Jin "Research on the evaluation of ink animation films based on ChipGAN", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 129161Q (20 October 2023); https://doi.org/10.1117/12.3005040

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KEYWORDS

RGB color model

Video

Color difference

Data modeling

Education and training

Image quality

Visualization

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