|Search by item||HOME > Access full text > Search by item|
JBE, vol. 23, no. 5, pp.718-721, September, 2018
Deep Learning based Inter Prediction Technique for Video Coding
Jeongkyung Lee, Nayoung Kim, and Je-Won Kang
C.A E-mail: firstname.lastname@example.org
This paper presents an inter-prediction technique using deep learning, where a virtual reference frame of the current frame is synthesized by using the reconstructed frames to improve coding efficiency. Experimental results demonstrate that the proposed algorithm provides 1.9% BD-rate reduction on average as compared to HEVC reference software in the Random Access condition.
Keyword: HEVC, deep learning, virtual reference frame
 S. Liu, L. Wang, P. Wu, and H. Yang, “JVET AHG report 9: Neural Networks in Video Coding (AHG9)” in ISO/IEC/JTC1/SC29/ WG11 and ITU-T SG16 Q.6, Apr. 2018.
 S.H Cho, Y. H. Kim, W. L, H.W. Kim, and C.S. Choi, “A Technical Analysis on Deep Learning based Image and Video Compression", Journal of Broadcast Engineering, Vol. 23, No. 3, 383-394, May 2018
 W. Park and M. Kim, "CNN-based in-loop filtering for coding efficiency improvement," IEEE Image, Video, and Multidimensional Signal Processing Workshop, 2016.
 J. Kang, S. Kim, and K. M. Lee, “Multi-modal Multi-scale Convolu- tional Neural Network based In-loop Filter Design for Next Generation Video Codec,” IEEE International Conference on Image Processing, 2017.
 J.K. Lee and J.-W. Kang, “Video coding technique based on deep learning”, 2018 KIBME Summer Conference.
 B. D. Brabandere, X. Jia, T. Tuytelaars, and L. V. Gool, "Dynamic filter networks," Neural Information Processing Systems (NIPS). 2016.
 S. Niklaus, L. Mai, and F. Liu. "Video frame interpolation via adaptive separable convolution," International Conference on Computer Vision, 2017.
 HM16.9 software, available at: https://hevc.hhi.fraunhofer.de/svn /svn_HEVCSoftware/tags/HM-16.9
 F. Bossen, “JCTVC-L1100: Common test conditions and software reference configurations” in ISO/IEC/JTC1/SC29/WG11 and ITU-T SG16 Q.6, Jan.2013.