Fast Screen Content Coding in HEVC Using Machine Learning

Screen Content (SC) videos require proper tools to handle their special characteristics since they include repeated regions, sharp edges, and limited number of colors. Therefore, SC extension to High Efficiency Video Coding (HEVC) standard has been released for this purpose. SC extension has new too...

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Autores principales:	Emad Badry, Koji Inoue, Mohammed Sharaf Sayed
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	High efficiency video coding (HEVC) screen content (SC) decision rule Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/c27c3ecca20940ac9605b4044c058772
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id	oai:doaj.org-article:c27c3ecca20940ac9605b4044c058772
record_format	dspace
spelling	oai:doaj.org-article:c27c3ecca20940ac9605b4044c0587722021-11-25T00:00:59ZFast Screen Content Coding in HEVC Using Machine Learning2169-353610.1109/ACCESS.2021.3125697https://doaj.org/article/c27c3ecca20940ac9605b4044c0587722021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605577/https://doaj.org/toc/2169-3536Screen Content (SC) videos require proper tools to handle their special characteristics since they include repeated regions, sharp edges, and limited number of colors. Therefore, SC extension to High Efficiency Video Coding (HEVC) standard has been released for this purpose. SC extension has new tools such as Intra Block Copy (IBC) and Palette (PLT) mode. These tools improve the coding efficiency, but they come with a huge computation complexity. In this paper, we propose a scheme to reduce the encoding time of SC encoder. It has two algorithms based on the Decision Rule (DR) machine learning technique. The first algorithm, which is called Mode Skipping (MS), is used to skip the unnecessary SC mode checking. Early Pruning Termination (EPT), which is the second algorithm, is used to stop the partitioning process early. A small number of features has to be calculated for the trained models. The proposed scheme was implemented then simulated by using the standard software test model SCM-6. The experimental results show that the time complexity is reduced by 37.84% on average while the Bjøntegaard delta bit-rate (BD-R) increases by 1.34% only.Emad BadryKoji InoueMohammed Sharaf SayedIEEEarticleHigh efficiency video coding (HEVC)screen content (SC)decision ruleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154659-154666 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	High efficiency video coding (HEVC) screen content (SC) decision rule Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	High efficiency video coding (HEVC) screen content (SC) decision rule Electrical engineering. Electronics. Nuclear engineering TK1-9971 Emad Badry Koji Inoue Mohammed Sharaf Sayed Fast Screen Content Coding in HEVC Using Machine Learning
description	Screen Content (SC) videos require proper tools to handle their special characteristics since they include repeated regions, sharp edges, and limited number of colors. Therefore, SC extension to High Efficiency Video Coding (HEVC) standard has been released for this purpose. SC extension has new tools such as Intra Block Copy (IBC) and Palette (PLT) mode. These tools improve the coding efficiency, but they come with a huge computation complexity. In this paper, we propose a scheme to reduce the encoding time of SC encoder. It has two algorithms based on the Decision Rule (DR) machine learning technique. The first algorithm, which is called Mode Skipping (MS), is used to skip the unnecessary SC mode checking. Early Pruning Termination (EPT), which is the second algorithm, is used to stop the partitioning process early. A small number of features has to be calculated for the trained models. The proposed scheme was implemented then simulated by using the standard software test model SCM-6. The experimental results show that the time complexity is reduced by 37.84% on average while the Bjøntegaard delta bit-rate (BD-R) increases by 1.34% only.
format	article
author	Emad Badry Koji Inoue Mohammed Sharaf Sayed
author_facet	Emad Badry Koji Inoue Mohammed Sharaf Sayed
author_sort	Emad Badry
title	Fast Screen Content Coding in HEVC Using Machine Learning
title_short	Fast Screen Content Coding in HEVC Using Machine Learning
title_full	Fast Screen Content Coding in HEVC Using Machine Learning
title_fullStr	Fast Screen Content Coding in HEVC Using Machine Learning
title_full_unstemmed	Fast Screen Content Coding in HEVC Using Machine Learning
title_sort	fast screen content coding in hevc using machine learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/c27c3ecca20940ac9605b4044c058772
work_keys_str_mv	AT emadbadry fastscreencontentcodinginhevcusingmachinelearning AT kojiinoue fastscreencontentcodinginhevcusingmachinelearning AT mohammedsharafsayed fastscreencontentcodinginhevcusingmachinelearning
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Fast Screen Content Coding in HEVC Using Machine Learning

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