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http://dx.doi.org/10.5909/JBE.2019.24.3.401

A Deep Learning based Inter-Layer Reference Picture Generation Method for Improving SHVC Coding Performance  

Lee, Wooju (Kwangwoon University)
Lee, Jongseok (Kwangwoon University)
Sim, Dong-Gyu (Kwangwoon University)
Oh, Seoung-Jun (Kwangwoon University)
Publication Information
Journal of Broadcast Engineering / v.24, no.3, 2019 , pp. 401-410 More about this Journal
Abstract
In this paper, we propose a reference picture generation method for Inter-layer prediction based deep learning to improve the SHVC coding performance. A description will be given of a structure for performing filtering using a VDSR network on a DCT-IF based upsampled picture to generate a new reference picture and a training method for generating a reference picture between SHVC Inter-layer. The proposed method is implemented based on SHM 12.0. In order to evaluate the performance, we compare the method of generating Inter-layer predictor by applying dictionary learning. As a result, the coding performance of the enhancement layer showed a bitrate reduction of up to 13.14% compared to the method using dictionary learning, a bitrate reduction of up to 15.39% compared to SHM, and a bitrate reduction of 6.46% on average.
Keywords
Scalable HEVC; CNN; Deep learning; Super resolution; Inter-layer prediction;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 H. Choi, K. Lee, J. Kang, S. Bae, and J. Yoo, "Overview and Performance Analysis of the Emerging Scalable Video Coding", Journal of Broadcast Engineering Vol.12, no. 6, pp. 542-554, November, 2007, http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE00926642.   DOI
2 J. M. Boyce. Y. Ye, J. Chen and A. K. Ramasubramonian, "Overview of SHVC: Scalable Extensions of the High Efficiency Video Coding Standard", IEEE Transactions on Circuits and Systems for Video Technology, Vol. 26, no. 1, pp. 20-34, July, 2016, https://doi.org/10.1109/TCSVT.2015.2461951.   DOI
3 J. Lee, J. Kang, H. Lee and J. Choi, "SHVC Standard Technology Trend," TTA Journal, vol.152, pp. 60-65, March, 2015, https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE06391006.
4 Y. Kim, J. Choi and H. Choi, "Performance Analysis of Scalable HEVC Coding Tools", JBE Vol.20, no. 4, pp. 497-508, July, 2015, https://doi.org/10.5909/JBE.2015.20.4.497.   DOI
5 J. Yang, J. Wright, T. S. Huang, and Y. Ma, "Image super-resolution via sparse representation", IEEE Transactions on Image Processing, vol. 19, no. 11, pp. 2861-2873, November 2010, https://doi.org/10.1109/TIP.2010.2050625.   DOI
6 J. Yang, Z. Wang, Z. Lin, S. Cohen, and T. Huang, "Coupled dictionary training for image super-resolution", IEEE Transactions on Image Processing, vol. 21, no. 8, pp. 3467-3478, Aug 2012, https://doi.org/10.1109/TIP.2012.2192127.   DOI
7 D. Chao, L. Chen, H. Kaiming and T. Xiaoou, "Image super-resolution using deep convolutional networks", IEEE transactions on pattern analysis and machine intelligence, Vol. 38, No.2. pp. 295-307, February, 2016, https://doi.org/10.1109/TPAMI.2015.2439281.   DOI
8 B. Lim, S. Son, H. Kim, S. Nah, and K. Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution", Proceedings of the the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Hawaii, USA, pp.136-144, July, 2017, https://arxiv.org/abs/1707.02921.
9 J. Kim, J. Lee and K. Lee, "Accurate Image Super-Resolution Using Very Deep Convolutional Networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, USA, pp. 1646-1654, June, 2016, https://doi.org/10.1109/CVPR.2016.182.
10 C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang and W. Shi, "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network", Proceedings of the the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Hawaii, USA, pp.4681-4690, July, 2017, https://arxiv.org/abs/1609.04802.
11 W. Park and M. Kim, "CNN-based in-loop filtering for coding efficiency improvement", Proceedings of the IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), IEEE, Bordeaux, France, July, 2016, https://doi.org/10.1109/IVMSPW.2016.7528223.
12 J. Schneider, J. Sauer, and M. Wien. "Dictionary learning based high frequency inter-layer prediction for scalable HEVC", Proceeding of IEEE Visual Communications and Image Processing (VCIP). IEEE, St. Petersburg, USA, pp.1-4, December, 2017, https://doi.org/10.1109/VCIP.2017.8305019.
13 V. Seregin and Y. He, "Common SHM test conditions and software reference configuration", Joint Collaborative Team on Video Coding(JCT-VC) of ITU-T SG16 WP3 and ISO/IEC JTC1/SC29/WG11 17th Meeting, JCTVC-Q1009, Valencia, Spain, pp. 1-4, March, 2014, http://phenix.it-sudparis.eu/jct/doc_end_user/current_document.php?id=9106.
14 J. Chen, J. Boyce, Y. Ye, M. Hannuksela and G. Barroux, "SHVC Test Model 11 (SHM 11)", Joint Collaborative Team on Video Coding (JCT-VC) of ITU-T SG16 WP3 and ISO/IEC JTC1/SC29/WG11, 113th Meeting, JCTVC-N15778, Geneva, Switzerland, pp. 1-12, October, 2015, https://mpeg.chiariglione.org/standards/mpeg-h/high-efficiency-video-coding/n15778-scalable-hevc-shvc-test-model-11-shm-11.