nvs-sqa 0.1.1
No-reference quality assessment for neurally synthesized scenes
Requires Python: >=3.6

# NVS-SQA

Official implementation of "NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without References" [arxiv](https://arxiv.org/abs/2501.06488)).


### Generating No-Reference Quality Representations with the Pretrained Model

#### Installation

You can install the package directly from PyPI:

```bash
pip install nvs-sqa
```

#### Usage

The package can be used programmatically. You can download the **examples folder** [here](https://github.com/VincentQQu/NVS-SQA/tree/main/examples). The quality scores are in format of JOD score, mostly negative values (offset by reference quality), with higher scores indicating better quality. Here's an example of how to generate quality features and scores:

```python
import nvs_sqa

# Initialize the QualityAssessor
qa = nvs_sqa.QualityAssessor()

# Define the evaluation folder path containing NSS directories
eval_folder = "./examples"

# Generate quality features
all_feats, save_path = qa.generate_quality_features(eval_folder, verbose=True)

# Compute quality scores
quality_scores = qa.generate_quality_scores(all_feats, verbose=True)

print(f"Features saved to: {save_path}")
```

#### About the Output

1. **Feature Generation and Saving**: The `generate_quality_features` method processes each NSS folder in the evaluation directory, generates quality features for each, and saves these features in a `.npz` file. The output is in the format of `{<nss_name>: 384-dim rep., ...}`. You can use `np.load()` to load it.

2. **Quality Scores**: The `generate_quality_scores` method calculates quality scores based on the generated features by applying a ridge linear regression model trained on the Fieldwork, Lab, and LLFF datasets.

3. **Example NSS**: You can find example NSS data in the repository's `examples` folder, which includes scenes generated by GNT-Cross-scene and Plenoxel.

#### Important Notes:

- **JOD Scoring Format**: The JOD score, which is the adopted scoring format, features primarily negative values (offset by reference quality), with higher scores indicating better quality.
- **Relevance of JOD Scores**: According to the dataset authors for Fieldwork and Lab, JOD scores are more meaningful within the same scene, suggesting that cross-scene comparisons of JOD scores may not provide meaningful insights.




### To Cite
<pre>

@article{qu2025nvs,
  title={NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without References},
  author={Qu, Qiang and Shen, Yiran and Chung, Yuk Ying and Cai, Weidong and Chen, Xiaoming and Liu, Tongliang},
  journal={arXiv preprint arXiv:2501.06488},
  year={2025}
}

@article{qu2024nerf,
  title={NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods},
  author={Qu, Qiang and Liang, Hanxue and Chen, Xiaoming and Chung, Yuk Ying and Shen, Yiran},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2024},
  publisher={IEEE}
}

@inproceedings{liang2024perceptual,
  title={Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views},
  author={Liang, Hanxue and Wu, Tianhao and Hanji, Param and Banterle, Francesco and Gao, Hongyun and Mantiuk, Rafal and {\"O}ztireli, Cengiz},
  booktitle={Computer Graphics Forum},
  volume={43},
  number={2},
  pages={e15036},
  year={2024},
  organization={Wiley Online Library}
}
</pre>