Ryosuke Hasegawa
ABSTRACT
We propose the Minecraft Video Aesthetic Quality Assessment Model (MCVQA), a tool designed to assess the aesthetic quality of Minecraft videos (MC videos) comprehensively. We explain the meticulous processes, including creating purpose-built datasets, implementing sophisticated feature extraction techniques, and fine-tuning model hyperparameters. We validate MCVQA’s efficacy through a two-step approach: accuracy validation and an information recommendation system. In conclusion, first, using the datasets created in this study, an impressive 63.6% alignment was observed between the "best" shot types assigned by the user and the model’s predictions. Subsequently, the usefulness of MCVQA as a recommendation system was validated, and a remarkable accuracy of 58.3% was confirmed.
- Omar Alawajee and Jonathan Delafield-Butt. 2021. Minecraft in education benefits learning and social engagement. International Journal of Game-Based Learning (IJGBL) 11, 4 (2021), 19–56.Google Scholar
Digital Library
- Sathya Veera Reddy Dendi and Sumohana S Channappayya. 2020. No-reference video quality assessment using natural spatiotemporal scene statistics. IEEE Transactions on Image Processing 29 (2020), 5612–5624.Google ScholarCross Ref
- Hao Jia and Ruck Thawonmas. 2022. Automatic Filming of Minecraft Settlements with Video Quality Assessment(in Japanese). Technical Report. Proceedings of Game Amusement Society 20th National Convention Osaka Electro-Communication University. 23–24 pages.Google Scholar
- Hao Jia, Ruck Thawonmas, and Pujana Paliyawan. 2021. An Aerial Cinematographer AI for Settlements in Minecraft–Toward Their Crowd Assessment. In 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE). IEEE, 853–854.Google ScholarCross Ref
- Jari Korhonen, Yicheng Su, and Junyong You. 2020. Blind natural video quality prediction via statistical temporal features and deep spatial features. In Proceedings of the 28th ACM International Conference on Multimedia. 3311–3319.Google ScholarDigital Library
- Jordan J Louviere, Terry N Flynn, and Anthony Alfred John Marley. 2015. Best-worst scaling: Theory, methods and applications. Cambridge University Press.Google Scholar
- Ioannis Mademlis, Nikos Nikolaidis, Anastasios Tefas, Ioannis Pitas, Tilman Wagner, and Alberto Messina. 2019. Autonomous UAV cinematography: A tutorial and a formalized shot-type taxonomy. ACM Computing Surveys (CSUR) 52, 5 (2019), 1–33.Google ScholarDigital Library
- Anush Krishna Moorthy and Alan Conrad Bovik. 2011. Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE transactions on Image Processing 20, 12 (2011), 3350–3364.Google Scholar
- Fu-Zhao Ou, Yuan-Gen Wang, and Guopu Zhu. 2019. A novel blind image quality assessment method based on refined natural scene statistics. In 2019 IEEE international conference on image processing (ICIP). IEEE, 1004–1008.Google ScholarCross Ref
- Christoph Salge, Claus Aranha, Adrian Brightmoore, Sean Butler, Rodrigo De Moura Canaan, Michael Cook, Michael Green, Hagen Fischer, Christian Guckelsberger, Jupiter Hadley, 2022. Impressions of the GDMC AI Settlement Generation Challenge in Minecraft. In Proceedings of the 17th International Conference on the Foundations of Digital Games. 1–16.Google ScholarDigital Library
- Christoph Salge, Michael Cerny Green, Rodgrigo Canaan, and Julian Togelius. 2018. Generative design in minecraft (gdmc) settlement generation competition. In Proceedings of the 13th International Conference on the Foundations of Digital Games. 1–10.Google ScholarDigital Library
- Marcello Stanco, Marco Lerro, and Giuseppe Marotta. 2020. Consumers’ preferences for wine attributes: A best-worst scaling analysis. Sustainability 12, 7 (2020), 2819.Google ScholarCross Ref
- Ruck Thawonmas, Ko Oda, and Tomonori Shuda. 2010. Rule-based camerawork controller for automatic comic generation from game log. In Entertainment Computing-ICEC 2010: 9th International Conference, ICEC 2010, Seoul, Korea, September 8-11, 2010. Proceedings 9. Springer, 326–333.Google Scholar
- Zhengzhong Tu, Xiangxu Yu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, and Alan C Bovik. 2021. RAPIQUE: Rapid and accurate video quality prediction of user generated content. IEEE Open Journal of Signal Processing 2 (2021), 425–440.Google ScholarCross Ref
- Yu Zhang, Xinbo Gao, Lihuo He, Wen Lu, and Ran He. 2019. Objective video quality assessment combining transfer learning with CNN. IEEE transactions on neural networks and learning systems 31, 8 (2019), 2716–2730.Google ScholarCross Ref
Index Terms
- Minecraft Video Aesthetics Quality Assessment Model
Recommendations
Technical Quality-Assisted Image Aesthetics Quality Assessment
Pattern Recognition and Computer VisionAbstractImage aesthetics assessment (IAA) aims at predicting the perceived aesthetic quality of images. Intuitively, the technical quality of an image has significant impact on its aesthetic quality, e.g., an image with noticeable distortions is not ...
Aesthetics, art, evolution
EvoMUSART'13: Proceedings of the Second international conference on Evolutionary and Biologically Inspired Music, Sound, Art and DesignThis paper discusses issues in evolutionary art related to Art Theory and Aesthetics with a view to better understanding how they might contribute to both research and practice. Aesthetics is a term often used in evolutionary art, but is regularly used ...
Comments