LNeRV: Learnable Hierarchical Encoding Improve Neural Representation Video Codec
Jiahong Chen, Xiang Liu, Bin Chen, Baoyi An, Tao Dai, Shu-Tao Xia
Abstract
Existing Implicit Neural Representation (INR) video compression techniques have opened up new avenues in the field of video compression. NeRV maps the temporal coordinates to high-resolution images using neural networks, providing a more flexible and efficient encoding method for video data. However, NeRV implicitly stores all video information in the network, requiring post network compression techniques such as pruning. To integrate explicit compression and implicit representation into an end-to-end framework, this study proposes a novel neural representation-based video compression paradigm called Latent code based Neural representation video compression (LNeRV). Specifically, LNeRV consists of hierarchy feature grids, synthesis network and entropy coding network. With a single-stage training process, LNeRV achieves video compression and dynamically allocates bits according to video complexity, better fitting dynamic videos. We provide a comprehensive compression-to-decompression workflow for our approach. Extensive experimental results verify the effectiveness of our LNeRV.
BibTeX
@inproceedings{icassp2025_lnervlearnablehi,
title = {LNeRV: Learnable Hierarchical Encoding Improve Neural Representation Video Codec},
author = {Jiahong Chen and Xiang Liu and Bin Chen and Baoyi An and Tao Dai and Shu-Tao Xia},
booktitle = {ICASSP 2025},
year = {2025}
}