A Projection-Based Surrogate Gradient Interpretation for Neural Codec Wrappers
Neural wrappers are learned pre-and postprocessing networks designed to enhance the performance of conventional video codecs. Although these approaches can significantly improve compression efficiency, training them remains challenging due to the non-differentiability of video codecs, which arises from the multiple discrete decisions involved in the encoding process. Surrogate gradients have recently emerged as an effective solution for enabling end-to-end learning with conventional codecs. They offer two main advantages: they avoid training an additional network to mimic the codec, and they can improve compression performance. In particular, the recently proposed SCALED method, which lev...
| Authors | Esteban Pesnel, Julien Le Tanou, Michael Ropert et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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