Improving HEVC Coding Efficiency Using Virtual Long-Term Reference Pictures.pdf (153.56 kB)
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Improving HEVC Coding Efficiency Using Virtual Long-Term Reference Pictures

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conference contribution
posted on 28.03.2022, 15:30 by Buddhiprabha Erabadda, Thanuja MallikarachchiThanuja Mallikarachchi, Gosala Kulupana, Anil Fernando
Inter-frame prediction in HEVC uses two types of reference pictures: short-term and long-term. Out of these long-term reference (LTR) pictures enable exploiting correlation among frames with extended temporal distances. In addition, LTR pictures improve the inter-frame prediction where video scenes are repeated such as in TV-series episodes, news broad-casts and movies. In this context, this paper proposes an algorithm to calculate LTR pictures using artificially generated virtual reference frames for static-camera scenes. The experimental results demonstrate an average coding improvement of2.34%in terms of Bjøntegaard Delta Bit Rate(BDBR), when compared with the HEVC reference encoder HM16.8.

Funding

This work was supported by the CONTENT4ALL project, which is funded under European Commission’s H2020 Framework Program (Grant number:762021).

History

Presented at

2020 IEEE 9th Global Conference on Consumer Electronics (GCCE 2020)

Published in

2020 IEEE 9th Global Conference on Consumer Electronics (GCCE 2020)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Citation

Erabadda, B., Mallikarachchi, T., Kulupana, G., Fernando, A. (2020) 'Improving HEVC Coding Efficiency Using Virtual Long-Term Reference Pictures', 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE 2020) Kobe: 13- 16th October 2020

Print ISSN

2378-8143

ISBN

978-1-7281-9802-6

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Thanuja Mallikarachchi

Copyright Holder

© The Publisher

Publisher Rights Statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Language

en