Abstract
The content produced by UE4 covers the fields of games, film and television, animation and even VR. As one of the most professional production software in the field of visual effects, Houdini can produce any kind of visual effects. The asset that can be used by UE4 becomes the fashion trend of today’s industry. Due to the characteristics of real-time rendering in UE4, how the special effects produced by Houdini can be smoothly displayed in UE4 and ensure a certain visual accuracy has become the focus of this paper. This paper uses ocean wave effects as a case, and with the help of the SSIM system, the accuracy value of the ocean wave effects in the state where the renderings of Houdini and UE4 are most similar are obtained. This paper uses different target polycount to test the import engine and obtains the most suitable polygon face number for the stable operation of UE4, which has high reference value and significance for the collaborative work of UE4 and Houdini. Also, can be ready for the pre-test of large-scale ocean wave effect visualization in the future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhou, J.N., Yun, T.S.: Visual restoration of ocean wave effects produced by Houdini in the engine. In: 12th International Conference on Computer Science and Information Technology 2019. T1029. University of Barcelona, Spain (2019)
Moorthy, A.K., Bovik, A.C.: Visual importance pooling for image quality assessment. IEEE J. Sel. Top. Signal Process. 3(2), 193–201 (2009)
Zhihu Web: Python image recognition-image similarity calculation. https://zhuanlan.zhihu.com/p/68215900. Accessed 20 July 2020
Bae, S., Kim, M.: A novel SSIM index for image quality assessment using a new luminance adaptation effect model in pixel intensity domain. In: Visual Communications and Image Processing (VCIP), Singapore, pp. 1–4 (2015). https://doi.org/10.1109/VCIP.2015.7457810
Girod, B.: What’s wrong with mean-squared error. In: Watson, A.B. (ed.) Digital Images and Human Vision, pp. 207–220. The MIT Press (1993)
Martens, J.-B., Meesters, L.: Image dissimilarity. Signal Process. 70, 155–176 (1998)
Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Commun. 43, 2959–2965 (1995)
Image Quality Assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 93 (2004)
Unreal Engine Documentation. https://www.unrealengine.com/zh-CN/tech-blog/real-physical-attributes-linghting-enviroment-in-ue4. Accessed 23 July 2020
Zhou, J.N., Jin, H.W., Beak, K.H., Yun, T.S.: Match move workflow for live-action production using game engine. In: International Conference on Culture Technology 2018, Shaoxing City, China, pp. 13–15 (2018). ISSN:2508-6561
Acknowledgment
This work was supported by Dongseo University, “Dongseo Cluster Project” Research Fund of 2020 (DSU-20200005).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, J.n., Beak, K.h., Lee, B.c., Yun, T.s. (2021). Visual Quality Comparison of Ocean Wave Effects Produced by Houdini in the Engine. In: Arai, K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1364. Springer, Cham. https://doi.org/10.1007/978-3-030-73103-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-73103-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73102-1
Online ISBN: 978-3-030-73103-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)