Low-Complexity Overfitted Neural Image Codec
Overview & Synopsis
We propose a neural image codec at reduced complexity which overfits the decoder parameters to each input image. While autoencoders perform up to a million multiplications per decoded pixel, the proposed approach only requires 2300 multiplications per pixel. Albeit low-complexity, the method rivals autoencoder performance and surpasses HEVC performance under various coding conditions. Additional lightweight modules and an improved training process provide a 14% rate reduction with respect to previous overfitted codecs, while offering a similar complexity. This work is made open-source at http://orange-opensource.github.io/Cool-Chic/.
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hal.science)
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Document Information
| Document Title | Low-Complexity Overfitted Neural Image Codec |
|---|---|
| Author / Organization | Thomas Leguay, Théo Ladune, Pierrick Philippe, Gordon Clare, Félix Henry, Olivier Déforges |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2023 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-14096 |
| Original Source Link | Visit Source Page on hal.science » |