QuantVSR
Low-Bit Post-Training Quantization for
Real-World Video Super-Resolution

Bowen Chai1*, Zheng Chen1*, Libo Zhu1, Wenbo Li2, Yong Guo3, Yulun Zhang1†
1Shanghai Jiao Tong University, 2The Chinese University of Hong Kong,
3Max Planck Institute for Informatics
*Equal Contribution. †Corresponding Author
arXiv Supplementary Code
Example Illustration

QuantVSR is a low-bit quantized model for real-world video super-resolution.

Abstract

Diffusion models have shown superior performance in real-world video super-resolution (VSR). However, the slow processing speeds and heavy resource consumption of diffusion models hinder their practical application and deployment. Quantization offers a potential solution for compressing the VSR model. Nevertheless, quantizing VSR models is challenging due to their temporal characteristics and high fidelity requirements. To address these issues, we propose QuantVSR, a low-bit quantization model for real-world VSR. We propose a spatio-temporal complexity aware (STCA) mechanism, where we first utilize the calibration dataset to measure both spatial and temporal complexities for each layer. Based on these statistics, we allocate layer-specific ranks to the low-rank full-precision (FP) auxiliary branch. Subsequently, we jointly refine the FP and low-bit branches to achieve simultaneous optimization. In addition, we propose a learnable bias alignment (LBA) module to reduce the biased quantization errors. Extensive experiments on synthetic and real-world datasets demonstrate that our method obtains comparable performance with the FP model and significantly outperforms recent leading low-bit quantization methods.

Method

Illustration of automated annotation pipeline

Overview of our QuantVSR. First, we analyze the temporal and spatial complexity distribution of the calibration dataset and leverage these statistics to allocate layer-specific ranks. Next, we jointly refine the two branches in spatio-temporal complexity aware mechanism. Finally, we train the learnable bias alignment module.

Results

Quantitative Results (click to expand)
  • Results on REDS4, SPMCS, and MVSR4x. (Tab. 3 of the main paper)

Qualitative Results (click to expand)
  • Results on REDS4, SPMCS, and MVSR4x. (Fig. 4 of the main paper)

BibTeX

@article{chai2025quantvsr,
  title = {QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution},
  author = {Chai, Bowen and Chen, Zheng and Zhu, Libo and Li, Wenbo and Guo, Yong and Zhang, Yulun},
  journal = {arXiv preprint arXiv:2508.04485},
  year = {2025}
}