FGA-NN: Film Grain Analysis Neural Network
This addresses the loss of aesthetic film grain during compression for media production, but it is incremental as it builds on existing parameter-based synthesis methods.
The paper tackled the problem of preserving film grain in compressed cinematographic content by introducing FGA-NN, a learning-based analysis method that estimates parameters for conventional synthesis, achieving a superior balance between accuracy and complexity.
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.