Username   Password       Forgot your password?  Forgot your username? 


Fast 3D-HEVC Coding based on Support Vector Machine

Volume 14, Number 9, September 2018, pp. 1968-1974
DOI: 10.23940/ijpe.18.09.p4.19681974

Hanqing Dinga, Shuaichao Weia, Yan Zhangb, and Qiuwen Zhanga

aCollege of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
bSchool of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang, 065000, China

(Submitted on May 25, 2018; Revised on July 17, 2018; Accepted on August 9, 2018)


3D high efficiency video coding (3D-HEVC) is the latest video compression standard for multi-view video systems. In this paper, a fast coding method is proposed by utilizing machine learning to alleviate the complexity of the 3D-HEVC system while maintaining the RD performance. The main content of our algorithm is to utilize the support vector machine (SVM) to analyze the motion properties of texture video where variable mode prediction is needed and early skip unnecessary modes for a given coding unit (CU). Experimental results confirmed that the proposed method could greatly lessen the computational complexity of the 3D-HEVC system with only a small BD-rate loss for texture view and synthesized view.


References: 9

              1. Y. Chen, G. Tech, K. Wegner, and S. Yea, “Test Model 11 of 3D-HEVC and MV-HEVC,” Joint Collaborative Team on 3D Video Coding Extensions (JCT-3V) document JCT3V-K1003, 11th Meeting, Geneva, CH, 12-18 February 2015
              2. H. R. Tohidypour, M. T. Pourazad, and P. Nasiopoulos, “A Low Complexity Mode Decision Approach for HEVC-based 3D Video Coding using a Bayesian Method,” in Proceedings of 2014 IEEE International Conference on Acoustic, Speech, Signal Processing (ICASSP), pp. 895-899, May 2014
              3. H. R. Tohidypour, M. T. Pourazad, and P. Nasiopoulos, “Online-Learning-based Complexity Reduction Scheme for 3D-HEVC,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 26 , No. 10, pp. 1870-1883, October 2016
              4. L. Shen, Z. Zhang, and Z. Liu, “Effective CU Size Decision for HEVC Intracoding,” IEEE Transactions on Image Processing, Vol. 23, No. 10, pp. 4232-4241, October 2014
              5. C. Chen and C. Lin, “LIBSVM: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology, Vol. 27, No. 2, pp. 1-27, 2011
              6. D. Zhong, H. J. Zhang, and S. F. Chang, “Clustering Methods for Video Browsing and Annotation,” in Proceedings of SPIE, pp. 239-246, March 1996
              7. A. Divakaran, A. Vetro, K. Asai, and H. Nishikawa, “Video Browsing System based on Compressed Domain Feature Extraction,” IEEE Transactions on Consumer Electronics, Vol. 46, No. 3, pp. 637-644, August 2000
              8. L. Shen, Z. Zhang, and Z. Liu, “Adaptive Inter-Mode Decision for HEVC Jointly Utilizing Inter-Level and Spatio-Temporal Correlations,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 24, No. 10, pp. 1709-1722, October 2014
              9. Q. Zhang, K. Huang, X. Wang, B. Jiang and Y. Gan, “Efficient Multiview Video Plus Depth Coding for 3D-HEVC based on Complexity Classification of the Treeblock,” Journal of Real-Time Image Processing, 10.1007/s11554-017-0692-5, May 2017


                          Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

                          Download this file (04-IJPE-09-04.pdf)04-IJPE-09-04.pdf[Fast 3D-HEVC Coding based on Support Vector Machine]383 Kb
                          This site uses encryption for transmitting your passwords.