Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions
Neural video representation (NeRV) has emerged as an efficient paradigm for video compression by encoding entire sequences into neural network parameters. Despite its strong reconstruction capability, NeRV suffers from high computational cost due to expensive convolutional operations in the decoder,...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | article |
| منشور في: |
2026
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/33302 |
| الوسوم: |
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| _version_ | 1864513431766302720 |
|---|---|
| author | Shanableh, Tamer |
| author_facet | Shanableh, Tamer |
| author_role | author |
| dc.creator.none.fl_str_mv | Shanableh, Tamer |
| dc.date.none.fl_str_mv | 2026-04-20T11:06:54Z 2026-04-20T11:06:54Z 2026-04-13 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Shanableh, T. (2026). Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions. IEEE Access, 1–1. https://doi.org/10.1109/access.2026.3683508 2169-3536 https://hdl.handle.net/11073/33302 10.1109/access.2026.3683508 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/access.2026.3683508 |
| dc.rights.none.fl_str_mv | Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.subject.none.fl_str_mv | Neural video representation Ghost convolution Deep video coding Deep learning |
| dc.title.none.fl_str_mv | Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Neural video representation (NeRV) has emerged as an efficient paradigm for video compression by encoding entire sequences into neural network parameters. Despite its strong reconstruction capability, NeRV suffers from high computational cost due to expensive convolutional operations in the decoder, limiting its applicability in resource-constrained environments. In this work, we propose GhostNeRV, an efficient extension of NeRV that reduces computational complexity through architectural optimization rather than numerical approximation. The work presents a systematic architectural efficiency study of structural redundancy within NeRV decoders, quantifying the trade-off between computational complexity, model compactness, and reconstruction fidelity. The proposed Ghost-NeRV integrates Ghost convolutions into the NeRV decoder to exploit feature redundancy and generate expressive representations using low-cost operations. Unlike binarization-based approaches, Ghost-NeRV preserves full-precision computation while significantly reducing the number of floating-point operations and model parameters. Extensive experiments on multiple video sequences demonstrate that Ghost-NeRV achieves up to 50% reduction in GFLOPs and approximately 19% reduction in model size, while maintaining stable reconstruction quality. Compared to the NeRV baseline, Ghost-NeRV incurs only a modest degradation in PSNR (typically within 1 dB) and preserves temporal consistency, significantly outperforming Binary-NeRV in perceptual stability. An ablation study further evaluates a GhostConv-V2 variant, which provides marginal quality improvements at the cost of increased computation and bitrate, confirming that the original Ghost convolution offers the best efficiency-accuracy trade-off. These results demonstrate that Ghost-NeRV provides an effective and practical solution for neural video representation, enabling substantial computational savings while maintaining high perceptual quality, improving its suitability for resourceconstrained environments. |
| format | article |
| id | aus_abd4edbc60d61464fc940d8fb130fde8 |
| identifier_str_mv | Shanableh, T. (2026). Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions. IEEE Access, 1–1. https://doi.org/10.1109/access.2026.3683508 2169-3536 10.1109/access.2026.3683508 |
| language_invalid_str_mv | en |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/33302 |
| publishDate | 2026 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| spelling | Ghost-NeRV: Efficient Neural Video Representation via Ghost ConvolutionsShanableh, TamerNeural video representationGhost convolutionDeep video codingDeep learningNeural video representation (NeRV) has emerged as an efficient paradigm for video compression by encoding entire sequences into neural network parameters. Despite its strong reconstruction capability, NeRV suffers from high computational cost due to expensive convolutional operations in the decoder, limiting its applicability in resource-constrained environments. In this work, we propose GhostNeRV, an efficient extension of NeRV that reduces computational complexity through architectural optimization rather than numerical approximation. The work presents a systematic architectural efficiency study of structural redundancy within NeRV decoders, quantifying the trade-off between computational complexity, model compactness, and reconstruction fidelity. The proposed Ghost-NeRV integrates Ghost convolutions into the NeRV decoder to exploit feature redundancy and generate expressive representations using low-cost operations. Unlike binarization-based approaches, Ghost-NeRV preserves full-precision computation while significantly reducing the number of floating-point operations and model parameters. Extensive experiments on multiple video sequences demonstrate that Ghost-NeRV achieves up to 50% reduction in GFLOPs and approximately 19% reduction in model size, while maintaining stable reconstruction quality. Compared to the NeRV baseline, Ghost-NeRV incurs only a modest degradation in PSNR (typically within 1 dB) and preserves temporal consistency, significantly outperforming Binary-NeRV in perceptual stability. An ablation study further evaluates a GhostConv-V2 variant, which provides marginal quality improvements at the cost of increased computation and bitrate, confirming that the original Ghost convolution offers the best efficiency-accuracy trade-off. These results demonstrate that Ghost-NeRV provides an effective and practical solution for neural video representation, enabling substantial computational savings while maintaining high perceptual quality, improving its suitability for resourceconstrained environments.American University of SharjahIEEE2026-04-20T11:06:54Z2026-04-20T11:06:54Z2026-04-13Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfShanableh, T. (2026). Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions. IEEE Access, 1–1. https://doi.org/10.1109/access.2026.36835082169-3536https://hdl.handle.net/11073/3330210.1109/access.2026.3683508enhttps://doi.org/10.1109/access.2026.3683508Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:repository.aus.edu:11073/333022026-04-21T06:07:39Z |
| spellingShingle | Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions Shanableh, Tamer Neural video representation Ghost convolution Deep video coding Deep learning |
| status_str | publishedVersion |
| title | Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions |
| title_full | Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions |
| title_fullStr | Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions |
| title_full_unstemmed | Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions |
| title_short | Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions |
| title_sort | Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions |
| topic | Neural video representation Ghost convolution Deep video coding Deep learning |
| url | https://hdl.handle.net/11073/33302 |