TY - GEN
T1 - Viewpoint quality evaluation for augmented virtual environment
AU - Meng, Ming
AU - Zhou, Yi
AU - Tan, Chong
AU - Zhou, Zhong
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Augmented Virtual Environment (AVE) fuses real-time video streaming with virtual scenes to provide a new capability of the real-world run-time perception. Although this technique has been developed for many years, it still suffers from the fusion correctness, complexity and the image distortion during flying. The image distortion could be commonly found in an AVE system, which is decided by the viewpoint of the environment. Existing work lacks of the evaluation of the viewpoint quality, and then failed to optimize the fly path for AVE. In this paper, we propose a novel method of viewpoint quality evaluation (VQE), taking texture distortion as evaluation metric. The texture stretch and object fragment are taken as the main factors of distortion. We visually compare our method with viewpoint entropy on campus scene, demonstrating that our method is superior in reflecting distortion degree. Furthermore, we conduct a user study, revealing that our method is suitable for the good quality demonstration with viewpoint control for AVE.
AB - Augmented Virtual Environment (AVE) fuses real-time video streaming with virtual scenes to provide a new capability of the real-world run-time perception. Although this technique has been developed for many years, it still suffers from the fusion correctness, complexity and the image distortion during flying. The image distortion could be commonly found in an AVE system, which is decided by the viewpoint of the environment. Existing work lacks of the evaluation of the viewpoint quality, and then failed to optimize the fly path for AVE. In this paper, we propose a novel method of viewpoint quality evaluation (VQE), taking texture distortion as evaluation metric. The texture stretch and object fragment are taken as the main factors of distortion. We visually compare our method with viewpoint entropy on campus scene, demonstrating that our method is superior in reflecting distortion degree. Furthermore, we conduct a user study, revealing that our method is suitable for the good quality demonstration with viewpoint control for AVE.
KW - Augmented Virtual Environment
KW - Depth estimation
KW - Semantic image segmentation
KW - Texture distortion
KW - Viewpoint quality evaluation
UR - https://www.scopus.com/pages/publications/85054508153
U2 - 10.1007/978-3-030-00764-5_21
DO - 10.1007/978-3-030-00764-5_21
M3 - 会议稿件
AN - SCOPUS:85054508153
SN - 9783030007638
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 223
EP - 234
BT - Advances in Multimedia Information Processing – PCM 2018 - 19th Pacific-Rim Conference on Multimedia, 2018, Proceedings
A2 - Ngo, Chong-Wah
A2 - Yamasaki, Toshihiko
A2 - Hong, Richang
A2 - Wang, Meng
A2 - Cheng, Wen-Huang
PB - Springer Verlag
T2 - 19th Pacific-Rim Conference on Multimedia, PCM 2018
Y2 - 21 September 2018 through 22 September 2018
ER -