FGA-NN: Film Grain Analysis Neural Network
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| Title: | FGA-NN: Film Grain Analysis Neural Network |
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| Authors: | Ameur, Zoubida, Lefebvre, Frédéric, De Lagrange, Philippe, Radosavljević, Miloš |
| Source: | 2025 IEEE International Conference on Image Processing (ICIP). :2103-2108 |
| Publication Status: | Preprint |
| Publisher Information: | IEEE, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Image and Video Processing, Computer Vision and Pattern Recognition |
| Description: | 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. |
| Document Type: | Article |
| DOI: | 10.1109/icip55913.2025.11084309 |
| DOI: | 10.48550/arxiv.2506.14350 |
| Access URL: | http://arxiv.org/abs/2506.14350 |
| Rights: | STM Policy #29 CC BY NC SA |
| Accession Number: | edsair.doi.dedup.....dfd2bef09d698c4c9167a96e53db5fab |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | 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. |
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| DOI: | 10.1109/icip55913.2025.11084309 |