---
license: cc-by-nc-4.0
language:
- en
pretty_name: BOCCHI Motion-Blur Detection
size_categories:
- 1K<n<10K
task_categories:
- image-segmentation
tags:
- motion-blur
- local-blur-detection
- DCT
- HiFST
- MSDCT-UNet
- accv2026
---

# BOCCHI Motion-Blur Detection

Companion dataset for the ACCV 2026 submission
*"MSDCT-UNet: Multi-Scale DCT U-Net for Local Motion Blur Detection"*.

This repo ships **both raw image+mask pairs and pre-computed PKL feature
caches**, so you can either train from scratch or skip preprocessing
entirely.

**Code release:** <https://huggingface.co/aianonymous12/msdct-unet>
(pretrained weights for the same architecture).

---

## Repository layout

```
aianonymous12/BOCCHI/
├── README.md
├── BOCCHI_dataset/                              ← OUR dataset (BOCCHI, CC BY-NC 4.0)
│   ├── img/   0001.jpg ... 0633.jpg          # 633 RGB images
│   ├── mask/  0001.png ... 0633.png          # 633 binary masks (0 = sharp, 255 = blur)
│   └── all_data.pkl                          # pre-built feature cache (8.4 GB)
├── Inference_dataset/merged/                  ← Cross-eval set (mixed sources)
│   ├── img_all/    422 jpg                   # 164 BOCCHI-protocol + 162 ReLoBlur test
│   ├── mask_all/   422 png                   #   + 96 BlurDataset held-out
│   └── all_data.pkl                          # pre-built (5.6 GB)
├── ReLoBlur_dataset/train/
│   └── all_data.pkl                          # pre-built only (16 GB) — third-party
└── BlurDataset/
    └── all_data.pkl                          # pre-built only (2.7 GB) — third-party
```

| Subset                | Samples | Raw size | PKL size |
|-----------------------|--------:|---------:|---------:|
| BOCCHI (BOCCHI_dataset) |     633 | 109 MB   | 8.4 GB   |
| Inference (merged)    |     422 |  97 MB   | 5.6 GB   |
| ReLoBlur (train)      |    1200 | —        | 16 GB    |
| BlurDataset           |     200 | —        | 2.7 GB   |

> Raw img+mask for BOCCHI and Inference are bundled (CC BY-NC 4.0).
> ReLoBlur and BlurDataset raw data are **not** included (third-party
> licenses); only the derived PKL features are redistributed.

---

## Quick start

```bash
pip install huggingface_hub

# Minimum: BOCCHI + Inference PKLs (14 GB) — reproduces Table 2 BOCCHI + Table 3
huggingface-cli download aianonymous12/BOCCHI --repo-type dataset \
    BOCCHI_dataset/all_data.pkl \
    Inference_dataset/merged/all_data.pkl \
    --local-dir data

# Raw img+mask only (205 MB, no PKLs) — for building your own features
huggingface-cli download aianonymous12/BOCCHI --repo-type dataset \
    --include "BOCCHI_dataset/img/*" "BOCCHI_dataset/mask/*" \
              "Inference_dataset/merged/img_all/*" "Inference_dataset/merged/mask_all/*" \
    --local-dir data

# Everything (~33 GB)
huggingface-cli download aianonymous12/BOCCHI --repo-type dataset --local-dir data
```

Then follow §4 of the code release README to reproduce paper Tables 2 / 3.

---

## PKL internal schema

```python
{
    "<stem>": {
        "img":      np.ndarray (H, W, 3)      uint8,          # RGB
        "msk":      np.ndarray (H, W)         uint8 {0,255},  # 0 = sharp, 255 = blur
        "DCT_coef": np.ndarray (Wgrid, Hgrid, 57)  float32,   # HiFST DCT features
    },
    "settings": {"mode": "rotate"/"pad", "size": (720, 1080),
                 "num_scales": 4, "scale_start": 2, ...}
}
```

The 57-channel layout is the legacy `(Wgrid, Hgrid, 57)` format; the loader
in the code release (`utils/dataset.py`) auto-detects this and permutes to
`(57, Hgrid, Wgrid)` PyTorch convention at `__getitem__` time.

---

## License

- **BOCCHI** (`BOCCHI_dataset/img/`, `BOCCHI_dataset/mask/`, `BOCCHI_dataset/all_data.pkl`)
  and the **BOCCHI-protocol portion of the Inference set**: CC BY-NC 4.0
  (our own data, free for non-commercial research use with attribution).
- **ReLoBlur** PKL: derivative of Li et al., AAAI 2023. Redistributed
  here under the original authors' terms for review purposes. Please cite
  the original ReLoBlur paper if you use it.
- **BlurDataset** PKL: derivative of the CUHK blur-detection benchmark.
  Same conditions as above.

The reviewer / reproducer must comply with each source dataset's license
when using the corresponding subset.

---

## Citation

```bibtex
@inproceedings{anon2026msdctunet,
  title  = {MSDCT-UNet: Multi-Scale DCT U-Net for Local Motion Blur Detection},
  author = {Anonymous},
  booktitle = {ACCV},
  year   = {2026}
}
```

## Note

Hosted on an anonymous reviewer account for the ACCV 2026 double-blind
review. Author identities will be revealed at camera-ready.