Frequency-aware Neural Representation for Videos

• URL: http://arxiv.org/abs/2601.17741v1
• Date: Sun, 25 Jan 2026 08:19:13 GMT
• Title: Frequency-aware Neural Representation for Videos
• Authors: Jun Zhu, Xinfeng Zhang, Lv Tang, Junhao Jiang, Gai Zhang, Jia Wang,
• Abstract summary: We propose FaNeRV, a Frequency-aware Neural Representation for videos.<n>FaNeRV explicitly decouples low- and high-frequency components to enable efficient and faithful video reconstruction.<n>Experiments on standard benchmarks demonstrate that FaNeRV significantly outperforms state-of-the-art INR methods.
• Score: 31.60687845071296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Implicit Neural Representations (INRs) have emerged as a promising paradigm for video compression. However, existing INR-based frameworks typically suffer from inherent spectral bias, which favors low-frequency components and leads to over-smoothed reconstructions and suboptimal rate-distortion performance. In this paper, we propose FaNeRV, a Frequency-aware Neural Representation for videos, which explicitly decouples low- and high-frequency components to enable efficient and faithful video reconstruction. FaNeRV introduces a multi-resolution supervision strategy that guides the network to progressively capture global structures and fine-grained textures through staged supervision . To further enhance high-frequency reconstruction, we propose a dynamic high-frequency injection mechanism that adaptively emphasizes challenging regions. In addition, we design a frequency-decomposed network module to improve feature modeling across different spectral bands. Extensive experiments on standard benchmarks demonstrate that FaNeRV significantly outperforms state-of-the-art INR methods and achieves competitive rate-distortion performance against traditional codecs.

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