ENCORE: Event-Assisted Complementary Motion Refinement for Learned Video Compression
Learned video compression relies on accurate temporal modeling to remove redundancy between adjacent frames. However, most existing codecs infer motion solely from discretely sampled RGB frames, making their estimates vulnerable to fast motion, blur, occlusion, weak texture, low illumination, and abrupt brightness changes. Event cameras asynchronously capture fine-grained intensity changes between RGB timestamps and therefore provide complementary evidence about inter-frame dynamics. We propose ENCORE, an Event-Assisted Complementary Motion Refinement framework for learned video compression. ENCORE first employs Complementary Motion Representation (CMR) to decompose aligned RGB-event feat...
| Authors | Shuhan Ye, Hongbin Yu, Chenqi Kong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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