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arXiv · 2606.28163

Enhanced Neural Video Representation Compression Across Extreme Complexity and Quality Scales

Ho Man Kwan·Tianhao Peng·Fan Zhang·Mike Nilsson·Andrew Gower·David Bull
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Abstract

Implicit neural representations (INRs) have recently emerged as a promising approach to video compression, delivering competitive rate-distortion performance alongside rapid decoding. However, existing neural video codecs struggle to balance complexity and scalability. Lightweight models often suffer from degraded compression performance when scaled to different bitrate/quality levels, whereas high-performance models exhibit limited scalability, as their model complexity typically increases with quality. This lack of a unified architecture capable of maintaining consistent complexity across a wide range of bitrates severely limits their diverse real-world deployment. To address these challenges, we introduce NVRC++, a novel INR-based video codec that utilizes a lightweight INR with multiple high-resolution feature grids, providing high scalability at any given complexity level. This is paired with an optimization framework that enables efficient overfitting on high-resolution grids for long video sequences, thereby exploiting spatio-temporal redundancies without prohibitive computational or memory overhead. Additionally, an advanced entropy model is designed for efficiently compressing the high-dimensional grid parameters. As a result, NVRC++ provides four complexity levels (from 7kMACs/pixel to 360kMACs/pixel), each spanning wide bitrate and quality ranges while supporting real-time decoding. The experimental results show that NVRC++ offers a much faster decoding speed (up to 8.5x) compared to the SOTA INR-based video codec, NVRC, while delivering comparable performance.

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Ho Man Kwan, Tianhao Peng, Fan Zhang, Mike Nilsson, Andrew Gower, David Bull. 2026-06-26. Enhanced Neural Video Representation Compression Across Extreme Complexity and Quality Scales. https://arxiv.org/abs/2606.28163

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