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arXiv · 2505.00046

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

Taiga Hayami·Kakeru Koizumi·Hiroshi Watanabe
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Abstract

Implicit Neural Representations (INRs) have garnered significant attention for their ability to model complex signals in various domains. Recently, INR-based frameworks have shown promise in neural video compression by embedding video content into compact neural networks. However, these methods often struggle to reconstruct high-frequency details under stringent constraints on model size, which are critical in practical compression scenarios. To address this limitation, we propose an INR-based video representation framework that integrates a general-purpose super-resolution (SR) network. This design is motivated by the observation that high-frequency components tend to exhibit low temporal redundancy across frames. By offloading the reconstruction of fine details to a dedicated SR network pre-trained on natural images, the proposed method improves visual fidelity. Experimental results demonstrate that the proposed method outperforms conventional INR-based baselines in reconstruction quality, while maintaining a comparable model size.

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Taiga Hayami, Kakeru Koizumi, Hiroshi Watanabe. 2025-04-30. SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution. https://arxiv.org/abs/2505.00046

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