Scalable Neural Video Representation Compression

• URL: http://arxiv.org/abs/2609.04273v1
• Date: Wed, 02 Sep 2026 21:25:47 GMT
• Title: Scalable Neural Video Representation Compression
• Abstract summary: Implicit neural representation (INR) based codecs achieve compression by overfitting a compact neural network to an individual video.<n>This paper proposes S-NVRC, a scalable INR-based video that jointly supports fine-grained and decoding complexity from a single embedded bitstream.
• Score: 21.36894041974722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scalable video coding (SVC) encodes a video into a layered bitstream consisting of a base layer and one or multiple enhancement layers, enabling decoding at different bitrate/quality/resolution operating points to accommodate diverse device capabilities and network conditions. Due to its practical flexibility, SVC has been incorporated into major video coding standards and has recently attracted growing interest for both scene-agnostic and scene-adaptive neural video codecs. Among the latter, Implicit neural representation (INR) based codecs achieve compression by overfitting a compact neural network to an individual video, offering fast decoding and competitive coding efficiency compared to scene-agnostic neural codecs. However, research on scalable INR-based compression remains in its infancy: these methods support scalable coding by introducing additional network layers, which couple the bitrate with the decoding complexity and also cannot achieve comparable performance with strong scalable/non-scalable codecs. In this context, this paper proposes S-NVRC, a scalable INR-based video codec that jointly supports fine-grained bitrate and decoding complexity scalability from a single embedded bitstream. It adopts a coarse-to-fine prefix for feature grids and a nested prefix for network layers, which scale bitrate and decoding complexity, respectively. The proposed S-NVRC spans a wide range of bitrate and decoding-complexity using a single encoding (training) and outperforms SHM 12.4 and the multi-layer VTM-20.0, by 43.7% and 5.6% in BD-rate on the UVG dataset, while also providing flexible complexity scalability. Implemented code will be provided.

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