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Graph-based transform based on 3D convolutional neural network for intra-prediction of imaging data
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Roy, Debaleena, Guha, Tanaya and Sanchez Silva, Victor (2022) Graph-based transform based on 3D convolutional neural network for intra-prediction of imaging data. In: 2022 Data Compression Conference (DCC), Snowbird, Utah, 22-25 Mar 2022. Published in: 2022 Data Compression Conference (DCC) doi:10.1109/DCC52660.2022.00029 ISSN 2375-0359.
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WRAP-Graph-based-transform-based-3D-convolutional-neural-network-intra-prediction-imaging-data-2022.pdf - Accepted Version - Requires a PDF viewer. Download (1774Kb) | Preview |
Official URL: https://doi.org/10.1109/DCC52660.2022.00029
Abstract
This paper presents a novel class of Graph-based Transform based on 3D convolutional neural networks (GBT-CNN) within the context of block-based predictive transform coding of imaging data. The proposed GBT-CNN uses a 3D convolutional neural network (3D- CNN) to predict the graph information needed to compute the transform and its inverse, thus reducing the signalling cost to reconstruct the data after transformation. The GBT- CNN outperforms the DCT and DCT/DST, which are commonly employed in current video codecs, in terms of the percentage of energy preserved by a subset of transform coefficients, the mean squared error of the reconstructed data, and the transform coding gain according
to evaluations on several video frames and medical images.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TS Manufactures |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Computer vision , Diagnostic imaging -- Data processing , Three-dimensional printing, Digital video -- Standards, Video compression -- Standards, Signal processing , Image processing | ||||||
| Journal or Publication Title: | 2022 Data Compression Conference (DCC) | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2375-0359 | ||||||
| Official Date: | 4 July 2022 | ||||||
| Dates: |
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| DOI: | 10.1109/DCC52660.2022.00029 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | © 2022 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: | 14 July 2022 | ||||||
| Date of first compliant Open Access: | 14 July 2022 | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 2022 Data Compression Conference (DCC) | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Snowbird, Utah | ||||||
| Date(s) of Event: | 22-25 Mar 2022 |
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