Fully Neural Network Mode Based Intra Prediction of Variable Block Size

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Intra prediction is an essential component in the image coding. This paper gives an intra prediction framework completely based on neural network modes (NM). Each NM can be regarded as a regression from the neighboring reference blocks to the current coding block. (1) For variable block size, we utilize different network structures. For small blocks 4×4 and 8×8, fully connected networks are used, while for large blocks 16×16 and 32×32, convolutional neural networks are exploited. (2) For each prediction mode, we develop a specific pre-trained network to boost the regression accuracy. When integrating into HEVC test model, we can save 3.55%, 3.03% and 3.27% BD-rate for Y, U, V components compared with the anchor. As far as we know, this is the first work to explore a fully NM based framework for intra prediction, and we reach a better coding gain with a lower complexity compared with the previous work.

本文言語English
ホスト出版物のタイトル2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ21-24
ページ数4
ISBN(電子版)9781728180670
DOI
出版ステータスPublished - 2020 12月 1
イベント2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020 - Virtual, Macau, China
継続期間: 2020 12月 12020 12月 4

出版物シリーズ

名前2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020

Conference

Conference2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020
国/地域China
CityVirtual, Macau
Period20/12/120/12/4

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 信号処理
  • メディア記述

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