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CDA-VSR

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[CVPR 2026] Compressed-Domain-Aware Online Video Super-Resolution

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# [CVPR 2026 Highlight] CDA-VSR: Compressed-Domain-Aware Online Video Super-Resolution

CDA-VSR is a compressed-domain-aware framework for online video super-resolution. 
 It explicitly exploits motion vectors, residual maps, and frame types from the compressed bitstream to improve both reconstruction quality and inference efficiency.

<a href="#news">News</a> •
 <a href="#overview">Overview</a> •
 <a href="#results">Results</a> •
 <a href="#repository structure">Repository Structure</a> •
 <a href="#how to use">How to use</a> •
 <a href="#citation">Citation</a>

---

## News

- **[2026/03]** Repository initialized.
- **[2026/03]** README released.
- **[2026/04]** Pretrained model and dataset released.

---

## Overview

Online video super-resolution (VSR) aims to recover high-resolution (HR) frames from low-resolution (LR) inputs under strict latency constraints, where only the current and previous frames are available. Most existing online VSR methods operate only on decoded LR frames and often suffer from expensive motion estimation or limited temporal modeling efficiency.

To address this issue, **CDA-VSR** explicitly introduces **compressed-domain priors** into online VSR, including:

- **Motion Vectors (MV)** for efficient coarse alignment
- **Residual Maps** for selective temporal fusion
- **Frame Types (I/P frames)** for adaptive reconstruction

Based on these priors, CDA-VSR contains three key modules:

- **MVGDA**: Motion-Vector-Guided Deformable Alignment
- **RMGF**: Residual Map Gated Fusion
- **FTAR**: Frame-Type-Aware Reconstruction

This design enables CDA-VSR to achieve a favorable trade-off between **restoration quality** and **runtime efficiency**.

## Results

### Quantitative Highlights

- On **REDS4**, CDA-VSR achieves approximately **90 FPS** while maintaining competitive or superior reconstruction quality.

### Qualitative Comparison

CDA-VSR reconstructs clearer edges and finer textures than representative online VSR baselines, especially in regions with large motion and compression artifacts.

## Repository Structure

```text
CDA-VSR/
├── basicsr/                      # Core framework
│   ├── archs/                    # Network architectures
│   ├── data/                     # Dataset definitions
│   ├── losses/                   # Loss functions
│   ├── metrics/                  # Evaluation metrics
│   ├── models/                   # Model wrappers
│   ├── train.py                  # Training entry
│   └── test.py                   # Testing entry
├── options/
│   ├── train/
│   │   └── train_CDA-VSR.yaml
│   └── test/
│       ├── test_CDA-VSR_REDS4.yaml
│       └── test_CDA-VSR_Inter4K.yaml
├──pretrained_models/
    └── best.pth
├── figures/                      # README figures
├── requirement.txt
├── LICENSE.txt
└── README.md
```
## Dataset and Pretrained Models

### Dataset

We provide the processed REDS compressed-domain dataset used in this work.

| Dataset | Description | Download |
|---|---|---|
| REDS_CRF18_23_28_new | Processed REDS dataset with compressed-domain information, including LR frames, motion vectors, residual maps, and frame-type information under different CRF settings. | [Baidu Netdisk](https://pan.baidu.com/s/1UHW5piWo2IS17N7ksgi73Q) |

Extraction code: `zc8e`

After downloading, please organize the dataset as follows:

```text
REDS_CRF18_23_28_new/
├── train/
└── test/
```

### Pretrained Models
```text
pretrained_models/
└── best.pth
```

## How to use
### Requirements

- Python 3.8+
- PyTorch 1.10+
- CUDA 11.x

### Training
Run training with:
```text
python basicsr/train.py -opt options/train/train_CDA-VSR.yaml
```
### Testing
#### Test on REDS4
Run training with:
```text
python basicsr/test.py -opt options/test/test_CDA-VSR_REDS4.yaml
```
#### Test on REDS4
Run evaluation with:
```text
python basicsr/test.py -opt options/test/test_CDA-VSR_Inter4K.yaml
```

## Citation
If you find this repository useful in your research, please cite:
```bibtex
@inproceedings{wang2026compressed,
  title={Compressed-Domain-Aware Online Video Super-Resolution},
  author={Wang, Yuhang and Li, Hai and Hou, Shujuan and Dong, Zhetao and Yang, Xiaoyao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2026}
}
```
## Contact
Please leave a issue or contact Yuhang Wang with bitwangyuhang@163.com

## License and Acknowledgement

This project is built upon [BasicSR](https://github.com/XPixelGroup/BasicSR) and [TMP](https://github.com/xtudbxk/TMP). We sincerely thank the authors for making their code publicly available.

CDA-VSR

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