Fully Neural Network Mode Based Intra Prediction of Variable Block Size

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages21-24
Number of pages4
ISBN (Electronic)9781728180670
DOIs
Publication statusPublished - 2020 Dec 1
Event2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020 - Virtual, Macau, China
Duration: 2020 Dec 12020 Dec 4

Publication series

Name2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020

Conference

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

Keywords

  • convolutional neural network
  • deep learning
  • fully connected layer
  • image compression
  • Intra prediction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Media Technology

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