NDLI: Performance analysis of machine learning for arbitrary downsizing of pre-encoded HEVC video

Content Provider	IEEE Xplore Digital Library
Author	Luong Pham Van De Praeter, J. Van Wallendael, G. De Cock, J. Van de Walle, R.
Copyright Year	1975
Abstract	Nowadays, broadcasters deliver ultra-high resolution video to their consumers. This live video is sent to a set-top box for display on a television. However, if one or more users in the home want to view the same video on their personal mobile devices with a lower display resolution and limited processing power, decoding the original ultra-high resolution video would result in stuttering and quickly drain the battery life on these devices. To enable a satisfactory consumer experience, the resolution of the video stream should be adapted to the target mobile device at the set-top box. The aim of this paper is to investigate the performance of different machine learning strategies to arbitrary downsize video pre-encoded with the high efficiency video coding standard (HEVC). These machine learning techniques exploit correlation between input and output coding information to predict the splitting behavior of HEVC coding units. Several machine learning algorithms are optimized. Additionally, both online and offline training strategies are tested. Of the tested algorithms, online-trained random forests achieve the best compression-efficiency with a bit rate increase of 5.4% and an average complexity reduction of 70%¹.
Sponsorship	IEEE Consumer Electronics Society
Starting Page	507
Ending Page	515
Page Count	9
File Size	626980
File Format	PDF
ISSN	00983063
Volume Number	61
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Transcoding Streaming media Predictive models Machine learning algorithms Complexity theory machine learning Video adaptation arbitrary downsizing highefficiency video coding
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Media Technology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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