Robust Video List Decoding in Error-Prone Transmission Systems Using a Deep Learning Approach
This paper introduces a novel deep-learning assisted video list decoding method for error-prone video transmission systems. Unlike traditional list decoding techniques, our proposed system uses a Transformer-based no-reference image quality assessment method to select the highest-scoring reconstruct...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10755955/ |
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| author | Yujing Zhang Stephane Coulombe Francois-Xavier Coudoux Alexis Guichemerre Patrick Corlay |
| author_facet | Yujing Zhang Stephane Coulombe Francois-Xavier Coudoux Alexis Guichemerre Patrick Corlay |
| author_sort | Yujing Zhang |
| collection | DOAJ |
| description | This paper introduces a novel deep-learning assisted video list decoding method for error-prone video transmission systems. Unlike traditional list decoding techniques, our proposed system uses a Transformer-based no-reference image quality assessment method to select the highest-scoring reconstructed video candidate after reception. Three new components are defined and used in the Transformer-assisted image quality evaluation metric: neighborhood-based patch fidelity aggregation, discriminant color texture transformation and ranking-constrained penalty loss function. We have also created our own database of non-uniformly distorted images, similar to those that might result from transmission errors, in a High Efficiency Video Coding (HEVC) context. In our specific testing context, our improved Transformer-assisted method has a decision accuracy of 100% for intra-coded image, while, for errors occurring in an inter image, it is 96%. Notably, in the few cases where a wrong choice is made, the selected candidate’s quality remains similar to the intact frame. Code: <uri>https://github.com/Yujing0926/Robust-Video-List-Decoding-Using-a-Deep-Learning-Approach</uri>. |
| format | Article |
| id | doaj-art-5b4e4bf62d9e4984bbbeb8c90b9a28bd |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5b4e4bf62d9e4984bbbeb8c90b9a28bd2025-08-20T02:50:25ZengIEEEIEEE Access2169-35362024-01-011217063217064710.1109/ACCESS.2024.350115210755955Robust Video List Decoding in Error-Prone Transmission Systems Using a Deep Learning ApproachYujing Zhang0https://orcid.org/0009-0003-9371-0502Stephane Coulombe1https://orcid.org/0000-0003-4495-3906Francois-Xavier Coudoux2https://orcid.org/0000-0002-5817-7429Alexis Guichemerre3https://orcid.org/0009-0008-0894-1901Patrick Corlay4https://orcid.org/0000-0002-3407-8805Department of Software and IT Engineering, École de technologie supérieure, Université du Québec, Montréal, QC, CanadaDepartment of Software and IT Engineering, École de technologie supérieure, Université du Québec, Montréal, QC, CanadaCNRS, UMR 8520, Département d’Opto-Acousto-Électronique (DOAE), Institut d’Électronique de Microélectronique et de Nanotechnologie (IEMN), Université Polytechnique Hauts-de-France, Valenciennes, FranceDepartment of Software and IT Engineering, École de technologie supérieure, Université du Québec, Montréal, QC, CanadaCNRS, UMR 8520, Département d’Opto-Acousto-Électronique (DOAE), Institut d’Électronique de Microélectronique et de Nanotechnologie (IEMN), Université Polytechnique Hauts-de-France, Valenciennes, FranceThis paper introduces a novel deep-learning assisted video list decoding method for error-prone video transmission systems. Unlike traditional list decoding techniques, our proposed system uses a Transformer-based no-reference image quality assessment method to select the highest-scoring reconstructed video candidate after reception. Three new components are defined and used in the Transformer-assisted image quality evaluation metric: neighborhood-based patch fidelity aggregation, discriminant color texture transformation and ranking-constrained penalty loss function. We have also created our own database of non-uniformly distorted images, similar to those that might result from transmission errors, in a High Efficiency Video Coding (HEVC) context. In our specific testing context, our improved Transformer-assisted method has a decision accuracy of 100% for intra-coded image, while, for errors occurring in an inter image, it is 96%. Notably, in the few cases where a wrong choice is made, the selected candidate’s quality remains similar to the intact frame. Code: <uri>https://github.com/Yujing0926/Robust-Video-List-Decoding-Using-a-Deep-Learning-Approach</uri>.https://ieeexplore.ieee.org/document/10755955/Video transmissionlist decodingnon-uniform distortionsno-reference image quality assessmentvision transformerconvolutional neural network |
| spellingShingle | Yujing Zhang Stephane Coulombe Francois-Xavier Coudoux Alexis Guichemerre Patrick Corlay Robust Video List Decoding in Error-Prone Transmission Systems Using a Deep Learning Approach IEEE Access Video transmission list decoding non-uniform distortions no-reference image quality assessment vision transformer convolutional neural network |
| title | Robust Video List Decoding in Error-Prone Transmission Systems Using a Deep Learning Approach |
| title_full | Robust Video List Decoding in Error-Prone Transmission Systems Using a Deep Learning Approach |
| title_fullStr | Robust Video List Decoding in Error-Prone Transmission Systems Using a Deep Learning Approach |
| title_full_unstemmed | Robust Video List Decoding in Error-Prone Transmission Systems Using a Deep Learning Approach |
| title_short | Robust Video List Decoding in Error-Prone Transmission Systems Using a Deep Learning Approach |
| title_sort | robust video list decoding in error prone transmission systems using a deep learning approach |
| topic | Video transmission list decoding non-uniform distortions no-reference image quality assessment vision transformer convolutional neural network |
| url | https://ieeexplore.ieee.org/document/10755955/ |
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