FGA-NN: Film Grain Analysis Neural Network
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916796835037184 |
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| author | Ameur, Zoubida Lefebvre, Frédéric De Lagrange, Philippe Radosavljević, Miloš |
| author_facet | Ameur, Zoubida Lefebvre, Frédéric De Lagrange, Philippe Radosavljević, Miloš |
| contents | Film grain, once a by-product of analog film, is now present in most cinematographic content for aesthetic reasons. However, when such content is compressed at medium to low bitrates, film grain is lost due to its random nature. To preserve artistic intent while compressing efficiently, film grain is analyzed and modeled before encoding and synthesized after decoding. This paper introduces FGA-NN, the first learning-based film grain analysis method to estimate conventional film grain parameters compatible with conventional synthesis. Quantitative and qualitative results demonstrate FGA-NN's superior balance between analysis accuracy and synthesis complexity, along with its robustness and applicability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FGA-NN: Film Grain Analysis Neural Network Ameur, Zoubida Lefebvre, Frédéric De Lagrange, Philippe Radosavljević, Miloš Computer Vision and Pattern Recognition Image and Video Processing Film grain, once a by-product of analog film, is now present in most cinematographic content for aesthetic reasons. However, when such content is compressed at medium to low bitrates, film grain is lost due to its random nature. To preserve artistic intent while compressing efficiently, film grain is analyzed and modeled before encoding and synthesized after decoding. This paper introduces FGA-NN, the first learning-based film grain analysis method to estimate conventional film grain parameters compatible with conventional synthesis. Quantitative and qualitative results demonstrate FGA-NN's superior balance between analysis accuracy and synthesis complexity, along with its robustness and applicability. |
| title | FGA-NN: Film Grain Analysis Neural Network |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2506.14350 |