Neural Representations for Videos (NeRV) have simplified the video codec process and achieved swift decoding speeds by encoding video content into a neural network, presenting a promising solution for video compression. However, existing work overlooks the crucial issue that videos reconstructed by these methods lack high-frequency details. To address this problem, this paper introduces a High-Frequency Enhanced Hybrid Neural Representation Network. Our method focuses on leveraging high-frequency information to improve the synthesis of fine details by the network. Specifically, we design a wavelet high-frequency encoder that incorporates Wavelet Frequency Decomposer (WFD) blocks to generate high-frequency feature embeddings. Next, we design the High-Frequency Feature Modulation (HFM) block, which leverages the extracted high-frequency embeddings to enhance the fitting process of the decoder. Finally, with the refined Harmonic decoder block and a Dynamic Weighted Frequency Loss, we further reduce the potential loss of high-frequency information. Experiments on the Bunny and UVG datasets demonstrate that our method outperforms other methods, showing notable improvements in detail preservation and compression performance.

翻译：视频神经表示（NeRV）通过将视频内容编码到神经网络中，简化了视频编解码过程并实现了快速解码速度，为视频压缩提供了一种有前景的解决方案。然而，现有研究忽视了一个关键问题：这些方法重建的视频缺乏高频细节。为解决这一问题，本文提出了一种高频增强混合神经表示网络。我们的方法侧重于利用高频信息来改善网络对精细细节的合成能力。具体而言，我们设计了一个小波高频编码器，该编码器包含小波频率分解器（WFD）模块以生成高频特征嵌入。接着，我们设计了高频特征调制（HFM）模块，利用提取的高频嵌入来增强解码器的拟合过程。最后，通过优化的谐波解码器模块和动态加权频率损失函数，我们进一步减少了高频信息的潜在损失。在Bunny和UVG数据集上的实验表明，我们的方法优于其他方法，在细节保留和压缩性能方面均显示出显著提升。