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A Real-Time Ultra-High Definition Video Decoder of AVS3 on Heterogeneous Systems

  • Xu Han
  • , Xiaofei Pan
  • , Shiqi Wang
  • , Shanshe Wang*
  • , Wen Gao
  • *Corresponding author for this work
  • Shanghai Jiao Tong University
  • Peking University
  • China Central Television
  • City University of Hong Kong
  • Peng Cheng Laboratory

Research output: Contribution to journalArticlepeer-review

Abstract

Recent years have witnessed the exponential increase in the demand for ultra high definition (UHD) video compression. The third generation of Audio Video Coding Standard (AVS3), which is also known as IEEE Standard 1857.10, is the latest audio and video coding standard developed by the China AVS working group. In AVS3, targeting for UHD videos, a series of efficient coding tools have been introduced, leading to the dramatical increase of computational burden. In this scenario, real-Time decoding of UHD videos becomes extremely challenging. This paper presents an improved hybrid CPU + GPU accelerated framework for AVS3 decoding. In particular, the motion vector (MV) derivation process is extracted from entropy decoding threads on the CPU. Therefore, the dependency between threads is removed and the entropy decoding can be performed by multiple threads efficiently. Regarding the GPU, we design compact data structures for transform, prediction, and in-loop filtering to reduce the burden of data transmission. A flexible information buffer supporting multi-Thread random writing is further created to coordinate the computation between CPU and GPU. Through asynchronous operations on the buffer, the computation of different computing units and the data transmission between them could be performed in parallel. With NVIDIA GeForce RTX 2080Ti GPU and Intel Core i7 8700K CPU, the proposed decoder achieves 151 frames per second (fps) for 4K videos and 55 fps for 8K videos in all intra configuration. In random access configuration, 218 fps and 74 fps are obtained for 4K and 8K videos, respectively.

Original languageEnglish
Pages (from-to)5595-5607
Number of pages13
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume32
Issue number8
DOIs
StatePublished - 1 Aug 2022
Externally publishedYes

Keywords

  • AVS3
  • GPU
  • parallel processing
  • video decoding

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