SOURCE-LINKED INTELLIGENCE
Video Compression with Graph-inspired Neural Representation
Implicit Neural Representations (INR) provide a compact and content-adaptive paradigm for video compression, typically representing a video through shared network parameters and frame-indexed embeddings. Compared to conventional or autoencoder-based codecs, these approaches exploit temporal redundancy within videos in an implicit manner, which potentially results in sub-optimal compression performance. In this paper, we propose G-NeRV, a graph-inspired INR that explicitly improves temporal redundancy exploitation in the implicit latent space. Motivated by the total correlation principles in in
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T12:43:03.000Z
First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.