Machine learning-based energy consumption modeling and comparison of H.264/AVC and Google VP8 encoders

Yousef Sharrab, Mohammad Alsmirat, Bilal Hawashin

Abstract


Advancement of the prediction models used in a variety of fields is a result of the contribution of machine learning approaches.Utilizing such modeling in feature engineering is exceptionally imperative and required. In this research, we show how to utilize machine learning to save time in research experiments, where we save more than five thousand hours of measuring the energy consumption of encoding recordings. Since measuring the energy consumption has got to be done by humans and since we require more than eleven thousand experiments to cover all the combinations of video sequences, video bit_rate, and video encoding settings, we utilize machine learning to model the energy consumption utilizing linear regression. VP8 codec has been offered by Google as an open video encoder in an effort to replace the popular MPEG-4 Part 10, known as H.264/AVC video encoder standard. This research model energy consumption and describes the major differences between H.264/AVC and VP8 encoders in terms of energy consumption and performance through experiments that are based on machine learning modeling. Twenty-nine raw video sequences are used, offering a wide range of resolutions and contents, with the frame sizes ranging from QCIF(176x144) to 2160p(3840x2160). For fairness in comparison analysis, we use seven settings in VP8 encoder and fifteen types of tuning in H.264/AVC. The settings cover various video qualities. The performance metrics include video qualities, encoding time, and encoding energy consumption.

Keywords


Feature Engineering;Encoders Comparison;Encoding Time;Encoding Energy Consumption;MPEG-4 Part 10;H.264/AVC;Perceptual Video Quality;Video Encoders;Video Compression;Google VP8 Encoder;Machine Learning;Regression;Modeling

References


Amir Mosavi and Abdullah Bahmani. Energy consumption prediction using machine learning; a review. 03 2019.

Holmes Wilson. Open letter to google: free vp8, and use it on youtube. 2010-03-12.

Chouitek Mama, Benouzza Noureddine, and Bekkouche Benaissa. Control of variable reluctance machine (8/6) by artificiel intelligence techniques. International Journal of Electrical & Computer Engineering (2088-8708), 10, 2020.

Pei Xie, Hong Zhang, Weike You, Xianfeng Zhao, Jianchang Yu, and Yi Ma. Adaptive vp8 steganography based on deblocking filtering. In Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, pages 25–30, 2019.

Mauricio Altamirano Silva, Raul Bertone, and J¨org Sch¨afer. Topology distribution for video-conferencing applications. In 2019 10th International Conference on Networks of the Future (NoF), Pages 90–97. IEEE, 2019.

Dr. Dmitriy Vatolin, Dr. Dmitriy Kulikov, and Alexander Parshin. Sixth MPEG-4 AVC/ H.264 video codecs comparison - short version.

Patrick Seeling, Frank H.P. Fitzek, Gerg¨o Ertli, Akshay Pulipaka, and Martin Reisslein. Video network traffic and quality comparison of VP8 and H.264 SVC. In Proceedings of the 3rd workshop on Mobile video delivery. Conference on Human Factors in Computing Systems, 2010.

Christian Feller, J¨urgen W¨unschmann, Thorsten Roll, and Albrecht Rothermel. The vp8 video codec - overview and comparison to h.264/avc. 2011 IEEE International Conference on Consumer Electronics -Berlin (ICCE-Berlin), pages 57–61, 2011.

Yousef O. Sharrab and Nabil J. Sarhan. Detailed comparative analysis of VP8 and H.264. In ISM, pages 133–140. IEEE, 2012. ISBN 978-1-4673-4370-1.

Yousef O. Sharrab and Nabil Sarhan. Accuracy and power consumption tradeoffs in video rate adaptation for computer vision applications. In ICME, pages 410–415. IEEE, 2012. ISBN 978-1-4673-1659-0.

ZhouWang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE TRANSACTIONS ON IMAGE PROCESSING, 13(4):600–612, 2004.

Iain E. G. Richardson. The H.264 Advanced Video Compression Standrad, second edition, WILEY, Published Online. Vcodex Limited, UK, 2010.

Jongho Kim, Donghyung Kim, and Jechang Jeong. Complexity reduction algorithm for intra mode selection in H.264/AVC video coding. Advanced Concepts for Intelligent Vision Systems ACIVS’06), 4179:454–465, 2006.

Jason Garrett-Glaser. The first in-depth technical analysis of VP8, http://x264dev.multimedia.cx/archives/377.

Yousef O. Sharrab and Nabil J. Sarhan. Power consumption modeling of capturing, encoding, and transmission in video streaming systems. Wayne State Multimedia Computing and Networking Research Lab- Technical Report, 2011.

Yousef O Sharrab and Nabil J Sarhan. Aggregate power consumption modeling of live video streaming systems. In Proceedings of the 4th ACM Multimedia Systems Conference, pages 60–71, 2013.

Yousef O Sharrab and Nabil Sarhan. Modeling and analysis of power consumption in live video streaming systems. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 13(4):1–25, 2017.

Alexis M. Tourapis, Oscar C. Au, and Ming L. Liou. Predictive motion vector field adaptive search technique (PMVFAST) – enhancing block based motion estimation. In Proceedings of Visual Communications and Image Processing Conference, 2001.

Jim Bankoski, Paul Wilkins, and Yaowu Xu. Technical overview of VP8, an open source video codec for the web.




DOI: http://doi.org/10.11591/ijece.v11i1.pp%25p
Total views : 0 times


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

ISSN 2088-8708, e-ISSN 2722-2578