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