Fast Partition Mode Decision via a Plug-in Fully Connected Network for Video Coding

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • Jiaqi Zhang
  • Chuanmin Jia
  • Qi Wang
  • Siwei Ma
  • Wen Gao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2022 Data Compression Conference (DCC)
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
PublisherIEEE
Pages222-231
ISBN (Electronic)978-1-6654-7893-9
Publication statusPublished - 2022

Publication series

NameData Compression Conference Proceedings
Volume2022-March
ISSN (Print)1068-0314

Conference

Title2022 Data Compression Conference (DCC 2022)
PlaceUnited States
CitySnowbird
Period22 - 25 March 2022

Abstract

Flexible coding unit partitioning such as quad-tree nested binary-tree and ternary-tree adopted by the emerging enhanced compression model (ECM) brings promising coding performance improvement. Meanwhile, the computational complexity increases dramatically, which may block the exploration and validation of new coding tools. This paper investigates a partition mode early pruning scheme via a fully connected network to reduce the encoding complexity for the ECM. In particular, we carefully select features and devise the fully connected network, which could seamlessly cooperate with the encoder, revealing promising learning and inference capability. Experimental results demonstrate that the proposed method achieves 15%50% encoding time savings with moderate bit-rate increasing on the ECM, and the extra complexity regarding the fully connected network and feature extraction is negligible.

Research Area(s)

  • Block Partition, ECM, QTMT

Citation Format(s)

Fast Partition Mode Decision via a Plug-in Fully Connected Network for Video Coding. / Zhang, Jiaqi; Wang, Meng; Jia, Chuanmin et al.

Proceedings - 2022 Data Compression Conference (DCC). ed. / Ali Bilgin; Michael W. Marcellin; Joan Serra-Sagrista; James A. Storer. IEEE, 2022. p. 222-231 (Data Compression Conference Proceedings; Vol. 2022-March).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review