Fast intra-coding unit partition decision in H.266/FVC based on deep learning

Gary, J.S., Jens-Rainer, O., Woo-Jin, H., Thomas, W.: Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22, 1649–1668 (2012)

Huang, H., Zhang, K., Huang, Y.W., Lei, S.M.: EE2.1: Quadtree plus binary tree structure integration with JEM tools. Joint Video Exploration Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, Doc. JVET-C0024, 3rd Meeting, Geneva (2016)

Gary, S., Jens-Rainer, O.: Meeting notes of the 2nd meeting of the Joint Video Exploration Team (JVET), document JVET-B\_Notes\_d7, San Diego (2016)

Zhao, W., Shiqi, W., Xinfeng, Z., Shanshe, W., Siwei, M.: Fast QTBT partitioning decision for inter frame coding with convolutional neural network. In: International Conference on Image Processing, IEEE (2018)

Ryu, S., Kang, J.: Machine learning-based fast angular prediction mode decision technique in video coding. IEEE Trans. Image Process. 27(11), 5525–5538 (2018)

Thomas, A., Alexandre, M., Wassim, H., Cyril, B., Daniel, M.: Random Forest oriented fast QTBT frame partitioning. In: International Conference on Acoustics, Speech and Signal Processing, IEEE (2019)

Ting Lan, L., Hui Yu, J., Jing Ya, H., Pao Chi, C.: Fast intra coding unit partition decision in H.266/FVC based on spatial features. J. Real-Time Image Process. (2018)

Wei, J., Hanjie, M., Yaowu, C.: Gradient based fast mode decision algorithm for intra prediction in HEVC. In: 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), IEEE, pp. 1836–1840 (2012)

Biao, M., Ray, C.C.C.: A ffast CU size decision algorithm for the HEVC intra encoder. IEEE Trans. Circuits Syst. Video Technol. 25(5), 892–896 (2015)

Alexandre, M., Florian, A., Maxime, P., Wassim, H., Daniel, M.: Prediction of quad-tree partitioning for budgeted energy HEVC encoding. In: 2017 International Workshop on Signal Processing Systems (SiPS), IEEE, pp. 1–6 (2017)

Fanyi, D., Zhan, M., Yao, W.: Fast CU partition decision using machine learning for screen content compression. In: 2015 International Conference on Image Processing (ICIP), IEEE, pp. 4972–4976 (2015)

Zongju, P., Chao, H., Fen, C., Gangyi, J., Xin, C., Mei, Y.: Multiple classifier-based fast coding unit partition for intra coding in future video coding. Signal Process. Image Commun. 78, 171–179 (2019)

Jin, Z., An, P., Yang, C., Shen, L.: Fast QTBT partition algorithm for intra frame coding through convolutional neural network. IEEE Access 6, 54660–54673 (2018)

Jin, Z., An, P., Shen, L., Yang, C.: CNN oriented fast QTBT partition algorithm for JVET intra coding. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4 (2017)

Ian, J., G., Jean, P., Mehdi, M., Bing, X., David, W., Sherjil, O., Aaron, C., Yoshua, B.: Generative adversarial nets. In: Proc. Conf. Neural Inf. Process. Syst., Palais des Congrs de Montreal, Montreal, pp. 2672–2680 (2014)

Shamsolmoali, P., Zareapoor, M., Wang, R., Jain, D.K., Yang, J.: G-GANISR: gradual generative adversarial network for image super resolution. Neurocomputing 366, 140–153 (2019)

Shamsolmoali, P., Zhang, J., Yang, J.: Image super resolution by dilated dense progressive network. Image Vis. Comput. 88, 9–18 (2019)

Linwei, Z., Sam, K., Yun, Z., Shiqi, W., Xu, W.: Generative Adversarial Network-Based Intra Prediction for Video Coding. IEEE transactions on multimedia 22(1) (2020)

Bross, B.: Versatile video coding (Draft 2), Joint Video Exploration Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC/ 29/WG 11, Tech. Rep. Doc. JVET-K1001-v7, Ljubljana, pp. 10–18 (2018)

Jianle, C., Ying, C., Marta, K., Xiang, L., Hongbin, L., Li, Z., Xin, Z.: Coding Tools Investigation for Next Generation Video Coding, ITU-T SG16 Doc.COM16–C806 (2015)

Marta, K., Jianle, C., Wei-Jung, C., Xiang, L., Amir, S., Li, Z., Xin, Z.: Study of Coding Efficiency Improvements Beyond HEVC, MPEG doc. m37102 (2015)

Segall, A., Francois, E., Rusanovskyy, D., and al.: JVET common test conditions and evaluation procedures for HDR/WCG video [C]//Proceedings of the Joint Video Exploration Team 5th Meeting, JVET, Geneva (2017)

Bjontegaard, G.: Calculation of average PSNR difference between RD-curves, ITU-T Q.6/SG16 VCEG 13th Meeting, DocumentVCEG-M33 (2001)

Duc-Tien, D.-N., Cecilia, P., Valentina, C., Giulia, B.: RAISE: A raw images dataset for digital image forensics. In: Proc. 6th ACM Multimedia Syst. Conf., pp. 219–224 (2015)

Tianyi, L., Mai, X., Xin, D., Zhenyu, G.: A deep convolutional neural network approach for complexity reduction on intra-mode HEVC. In: International Conference on Multimedia and Expo, IEEE, pp. 1255–1260 (2017)

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

Geoffrey, E.H., Nitish, S., Alex, K., Ilya, S., Ruslan, R.S.: Improving neural networks by preventing coadaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)

JVET Software Repository, [online]. https://jvet.hhi.fraunhofer.de/svn/svn_HMJEMSoftware/branches/HM-16.6-JEM-7.0-dev/

Jill, B., Karsten, S., Xiang, L.: JVETJ1010: JVET common test conditions and software reference configurations (2018)

JVETSoftwarRepositry [online]. https://jvet.hhi.fraunhofer.de/svn/svn_HMJEMSoftware/branchesHM-16.6-JEM-3.1 dev/

Martin, A., Ashish, A., Paul, B., Eugene, B., Zhifeng, C., Craig, C., Greg, S.C., Andy, D., Jeffrey, D., Matthieu, D., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems, arXiv preprint arXiv:1603.04467 (2016)

Fernández, D.G., Del Barrio, A.A., Botella, G., Meyer-Baese, U., Meyer-Baese, A., Grecos, C.: Information fusion based techniques for HEVC. In: Real-Time Image and Video Processing 2017 10223, 102230M (2017)