Camera Position Optimization Using the Effective Area In Light Field Rendering
Authors: 
Linze Wu, Li Yu, Changjian ZhuAuthors Info & Claims
Linze Wu, Li Yu, Changjian ZhuAuthors Info & ClaimsPages 60 - 65
Abstract
In light field rendering, images captured by cameras are used to render virtual views. The changes in camera positions lead to different rendering quality. To improve rendering quality, it is necessary to optimize the camera position. This paper proposes a mathematical model based on a definition called effective area (EA) to rearrange cameras. EA connects camera position with the rendering error so as to quantify the suitability of a camera position for a given arrangement of virtual views. Using the EA, we present an optimization algorithm for camera position to yield the optimal quality. Simulate results demonstrate that EA can be used to quantify the rendering quality. We also show the optimization results of different arrangement of virtual views.
References
[1]
The stanford 3d scanning repository. https://graphics.stanford.edu/data/3Dscanrep/.
[2]
Y. Gao, M. Wang, R. Ji, X. Wu, and Q. Dai. 3d object retrieval with hausdorff distance learning. IEEE Trans. Industrial Electronics., 61(4):2088--2098, 2014.
[3]
Y. Gao, M. Wang, D. Tao, R. Ji, and Q. Dai. 3d object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Processing., 21(9):4290--4303, 2012.
[4]
Y. Gao, M. Wang, Z. Zha, Q. Tian, Q. Dai, and N. Zhang. Less is more: Efficient 3d object retrieval with query view selection. IEEE Trans. Multimedia., 11(5):1007--1018, 2011.
[5]
R. Ji, L.-Y. Duan, J. Chen, T. Huang, and W. Gao. Mining compact 3d patterns for low bit rate mobile visual search. IEEE Trans. Image Processing., 2014.
[6]
R. Ji, L.-Y. Duan, J. Chen, H. Yao, J. Yuan, Y. Rui, and W. Gao. Location discriminative vocabulary coding for mobile landmark search. International Journal of Computer Vision, 96(3):290--314, 2012.
[7]
R. Ji, L.-Y. Duan, H. Yao, L. Xie, Y. Rui, and W. Gao. Learning to distribute vocabulary indexing for scalable visual search. IEEE Trans. Multimedia., 15(1):153--166, 2013.
[8]
R. Ji, Y. Gao, R. Hong, Q. Liu, D. Tao, and X. Li. Spectral-spatial constraint hyperspectral image classification. IEEE Trans. Geoscience and Remote Sensing., 52(3):1811--1824, 2014.
[9]
R. Ji, H. Yao, W. Liu, X. Sun, and Q. Tian. Task dependent visual codebook compression. IEEE Trans. Image Processing., 21(4):2282--2293, 2012.
[10]
R. Ji, H. Yao, and X. Sun. Actor-independent action search using spatiotemporal vocabulary with appearance hashing. Pattern Recognition, 44(3):624--638, 2011.
[11]
Z. Lin and H.-Y. Shum. A geometric analysis of light field rendering. International Journal of Computer Vision, 58(2):121--138, 2004.
[12]
Q. Liu, Y. Yang, R. Ji, Y. Gao, and L. Yu. Cross-view down/up-sampling method for multiview depth video coding. IEEE Signal Processing Letters, 19(5):295--298, 2012.
[13]
S. Liu, P. An, Z. Zhang, Q. Zhang, L. Shen, and G. Jiang. On the relationship between multi-view data capturing and quality of rendered virtual view. The Imaging Science Journal, 57(2):250--259, 2009.
[14]
H. T. Nguyen and M. N. Do. Error analysis for image-based rendering with depth information. IEEE Trans. Image Processing., 18(4):703--716, 2009.
[15]
F. Safaei, P. Mokhtarian, H. Shidanshidi, W. Li, M. Namazi-Rad, and A. Mousavinia. Scene-adaptive configuration of two cameras using the correspondence field function. In Proceedings of the 2013 IEEE International Conference on Multimedia and Expo (ICME), pages 1--6. IEEE, 2013.
[16]
H. Shidanshidi, F. Safaei, and W. Li. Objective evaluation of light field rendering methods using effective sampling density. In Proceedings of the 2011 IEEE 13th International Workshop on Multimedia Signal Processing (MMSP), pages 1--6. IEEE, 2011.
[17]
H. Shidanshidi, F. Safaei, and W. Li. A quantitative approach for comparison and evaluation of light field rendering techniques. In Proceedings of the 2011 IEEE International Conference on Multimedia and Expo (ICME), pages 1--4. IEEE, 2011.
[18]
Z. Yan, L. Yu, Y. Yang, and Q. Liu. Beyond the interference problem: hierarchical patterns for multiple-projector structured light system. Applied Optics, 53(15):1--13, 2014.
[19]
Y. Yang and Q. Dai. Contourlet-based image quality assessment for synthesised virtual image. Electronics letters, 46(7):492--494, 2010.
[20]
Y. Yang, X. Wang, T. Guan, and J. Shen. A multidimensional image preference prediction model for user generated images in social networks. Information Sciences, 2014.
[21]
C. Zhang and T. Chen. A survey on image-based rendering-representation, sampling and compression. Signal Processing: Image Communication, 19(1), 2004.
[22]
C. Zhang and T. Chen. Active rearranged capturing of image-based rendering scenesąłtheory and practice. IEEE Trans. Multimedia., 9(3):520--531, 2007.
[23]
C. Zhu, L. Yu, and P. Zhou. Coverage field analysis to the quality of light field rendering. In Proceedings of the 2014 Springer International Conference on MultiMedia Modeling, pages 170--180. Springer, 2014.
Index Terms
- Camera Position Optimization Using the Effective Area In Light Field Rendering
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Information & Contributors
Information
Published In
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
- General Chairs:
- Hanzi Wang,
- Larry Davis,
- Program Chairs:
- Wenwu Zhu,
- Stephan Kopf,
- Yanyun Qu,
- Publications Chairs:
- Jun Yu,
- Jitao Sang,
- Tao Mei
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In-Cooperation
- NSF of China: National Natural Science Foundation of China
- Beijing ACM SIGMM Chapter
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 July 2014
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ICIMCS '14
ICIMCS '14: International Conference on Internet Multimedia Computing and Service
July 10 - 12, 2014
Xiamen, China
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Overall Acceptance Rate 163 of 456 submissions, 36%
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