Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Graph-based transform based on neural networks for intra-prediction of imaging data

Tools
- Tools
+ Tools

Roy, Debaleena, Guha, Tanaya and Sanchez Silva, Victor (2021) Graph-based transform based on neural networks for intra-prediction of imaging data. In: IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2021), Gold Coast, Queensland, Australia, 25-28 Oct 2021. Published in: IEEE Xplore doi:10.1109/MLSP52302.2021.9596317

[img]
Preview
PDF
WRAP-graph-based-transform-based-neural-networks-intra-prediction-imaging-data-2021.pdf - Accepted Version - Requires a PDF viewer.

Download (2223Kb) | Preview
Official URL: https://doi.org/10.1109/MLSP52302.2021.9596317

Request Changes to record.

Abstract

This paper introduces a novel class of Graph-based Trans-form based on neural networks (GBT-NN) within the con-text of block-based predictive transform coding of imaging data. To reduce the signalling overhead required to recon-struct the data after transformation, the proposed GBT-NN predicts the graph information needed to compute the inverse transform via a neural network. Evaluation results on several video frames and medical images, in terms of the percentage of energy preserved by a sub-set of transform coefficients and the mean squared error of the reconstructed data, show that the GBT-NN can outperform the DCT and DST, which are widely used in modern video codecs.

Item Type: Conference Item (Paper)
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Faculty of Science, Engineering and Medicine > Engineering > Engineering
Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH): Neural networks (Computer science), Machine learning, Signal processing , Image compression , Image processing -- Digital techniques , Video compression
Journal or Publication Title: IEEE Xplore
Publisher: IEEE
Official Date: 15 November 2021
Dates:
DateEvent
15 November 2021Published
15 August 2021Accepted
DOI: 10.1109/MLSP52302.2021.9596317
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2021 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.
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 11 November 2021
Date of first compliant Open Access: 12 November 2021
Conference Paper Type: Paper
Title of Event: IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2021)
Type of Event: Workshop
Location of Event: Gold Coast, Queensland, Australia
Date(s) of Event: 25-28 Oct 2021
Related URLs:
  • Publisher
  • Organisation

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us