# On Quantizing Neural Representation for Variable-Rate Video Coding

arXiv: https://arxiv.org/abs/2502.11729 · HTML: https://mlsanity.com/paper/2502.11729/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Cat: Post-Training Quantization Error Reduction via Cluster-based Affine Transformation](https://mlsanity.com/paper/2509.26277/) (2025)
- [High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning](https://mlsanity.com/paper/2606.13957/) (2026)
- [Hadamard-Domain Model Quantization for Learned Image Coding](https://mlsanity.com/paper/2608.01653/) (2026)

## Abstract (arXiv)

This work introduces NeuroQuant, a novel post-training quantization (PTQ) approach tailored to non-generalized Implicit Neural Representations for variable-rate Video Coding (INR-VC). Unlike existing methods that require extensive weight retraining for each target bitrate, we hypothesize that variable-rate coding can be achieved by adjusting quantization parameters (QPs) of pre-trained weights. Our study reveals that traditional quantization methods, which assume inter-layer independence, are ineffective for non-generalized INR-VC models due to significant dependencies across layers. To address this, we redefine variable-rate INR-VC as a mixed-precision quantization problem and establish a theoretical framework for sensitivity criteria aimed at simplified, fine-grained rate control. Additionally, we propose network-wise calibration and channel-wise quantization strategies to minimize quantization-induced errors, arriving at a unified formula for representation-oriented PTQ calibration. Our experimental evaluations demonstrate that NeuroQuant significantly outperforms existing techniques in varying bitwidth quantization and compression efficiency, accelerating encoding by up to eight times and enabling quantization down to INT2 with minimal reconstruction loss. This work introduces variable-rate INR-VC for the first time and lays a theoretical foundation for future research in rate-distortion optimization, advancing the field of video coding technology. The materials will be available at https://github.com/Eric-qi/NeuroQuant.