High-Frequency Enhanced Hybrid Neural Representation for Video Compression

• URL: http://arxiv.org/abs/2411.06685v1
• Date: Mon, 11 Nov 2024 03:04:46 GMT
• Title: High-Frequency Enhanced Hybrid Neural Representation for Video Compression
• Authors: Li Yu, Zhihui Li, Jimin Xiao, Moncef Gabbouj,
• Abstract summary: 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. Experiments on the Bunny and UVG datasets demonstrate that our method outperforms other methods.
• Score: 32.38933743785333
• License:
• Abstract: 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.

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