NeuralLVC: Neural Lossless Video Compression via Masked Diffusion with Temporal Conditioning
While neural lossless image compression has advanced significantly with learned entropy models, lossless video compression remains largely unexplored in the neural setting. We present NeuralLVC, a neural lossless video codec that combines masked diffusion with an I/P-frame architecture for exploiting temporal redundancy. Our I-frame model compresses individual frames using bijective linear tokenization that guarantees exact pixel reconstruction. The P-frame model compresses temporal differences between consecutive frames, conditioned on the previous decoded frame via a lightweight reference embedding that adds only 1.3% trainable parameters. Group-wise decoding enables controllable speed-...
| Authors | Tiberio Uricchio, Marco Bertini |
|---|---|
| Primary category | eess.IV |
| Published | 3 April 2026 |
| Last updated at source | 8 September 2026 |
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