NeuralLVC: Neural Lossless Video Compression via Masked Diffusion with Temporal Conditioning

• URL: http://arxiv.org/abs/2604.03353v1
• Date: Fri, 03 Apr 2026 16:49:42 GMT
• Title: NeuralLVC: Neural Lossless Video Compression via Masked Diffusion with Temporal Conditioning
• Abstract summary: We present NeuralLVC, a neural lossless video that combines masked diffusion with an I/P-frame architecture for exploiting temporal redundancy.<n>Experiments on 9 Xiph CIF sequences show that NeuralLVC outperforms H..264 and H.265 by a significant margin.
• Score: 8.961948904211619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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-compression trade-offs. Our codec is lossless in the input domain: for video, it reconstructs YUV420 planes exactly; for image evaluation, RGB channels are reconstructed exactly. Experiments on 9 Xiph CIF sequences show that NeuralLVC outperforms H.264 and H.265 lossless by a significant margin. We verify exact reconstruction through end-to-end encode-decode testing with arithmetic coding. These results suggest that masked diffusion with temporal conditioning is a promising direction for neural lossless video compression.

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