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license: cc-by-nc-sa-4.0
---
# DocQT - Improving Document Forgery Localization Robustness via Diverse JPEG Quantization-Tables
Repository containing JPEG quantization tables used for the Real-QT protocol.
Dataset name: DocQT.
Only header-extracted quantization matrices are provided.
## Paper
- arXiv: https://arxiv.org/abs/2605.19688
## Citation
If you use DocQT, this quantization-table repository, or build upon our article, please cite our paper:
```bibtex
@misc{ronfleuxcorail2026docqt,
title={DocQT: Improving Document Forgery Localization Robustness via Diverse JPEG Quantization Tables},
author={Kylian Ronfleux-Corail and Guillaume Bernard and Mickael Coustaty and Nicolas Sidere},
year={2026},
eprint={2605.19688},
archivePrefix={arXiv},
primaryClass={cs.CV},
doi={10.48550/arXiv.2605.19688},
url={https://arxiv.org/abs/2605.19688}
}
```
## Available quantization tables
- Luminance tables: `quantification_luminance.json`
- Number of tables: 859
- Chrominance tables: `quantification_chrominance.json`
- Number of tables: 294
## File format
Both files are stored in JSON for better portability and long-term compatibility.
- Root object: a list of quantization tables
- One table: a flat list of 64 integers
- Table values: integers
In other words, each file follows this structure:
- list[table]
- table = list[64 integer values]
## Example: use quantization tables with Pillow JPEG compression
```python
import json
from pathlib import Path
from PIL import Image
def load_quantization_tables(base_dir: str = ".") -> tuple[list, list]:
base_path = Path(base_dir)
luminance_tables = json.loads(
(base_path / "quantification_luminance.json").read_text(encoding="utf-8")
)
chrominance_tables = json.loads(
(base_path / "quantification_chrominance.json").read_text(encoding="utf-8")
)
return luminance_tables, chrominance_tables
luminance_tables, chrominance_tables = load_quantization_tables(".")
# Choose the table indices you want to use for compression.
luma_idx = 0
chroma_idx = 0
luma_qtable = luminance_tables[luma_idx]
chroma_qtable = chrominance_tables[chroma_idx]
with Image.open("input.png") as image:
image = image.convert("RGB")
image.save(
"output_custom_qtables.jpg",
format="JPEG",
qtables=[luma_qtable, chroma_qtable],
subsampling="4:2:0",
optimize=True,
)
# Note: avoid passing a quality value if you want to keep your custom qtables as-is.
```
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