SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution

Implicit Neural Representations (INRs) have garnered significant attention for their ability to model complex signals across a variety of domains. Recently, INR-based approaches have emerged as promising frameworks for neural video compression. While conventional methods primarily focus on embedding video content into compact neural networks for efficient representation, they often struggle to reconstruct high-frequency details under stringent model size constraints, which are critical in practical compression scenarios. To address this limitation, we propose an INR-based video representation method that integrates a general-purpose super-resolution (SR) network. Motivated by the observation that high-frequency components exhibit low temporal redundancy across frames, our method entrusts the reconstruction of fine details to the SR network. Experimental results demonstrate that the proposed method outperforms conventional INR-based baselines in terms of reconstruction quality, while maintaining comparable model sizes.

@article{hayami2025_2505.00046,
  title={ SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution },
  author={ Taiga Hayami and Kakeru Koizumi and Hiroshi Watanabe },
  journal={arXiv preprint arXiv:2505.00046},
  year={ 2025 }
}