---
license: cc0-1.0
tags:
- sisr
pretty_name: Public Single Image Super-Resolution Dataset — Validation 125
---

# PSISRD_VAL125

**Public Single Image Super-Resolution Dataset — Validation Set (125 Images)**  
An **open, CC0-licensed validation dataset** for benchmarking and evaluating Single Image Super-Resolution (SISR) models.  
Freely available for research, development, and commercial use — no attribution required.

## 🖼️ Example Subset

Below is an example showing the **first 36 images** from the PSISRD_VAL125 dataset.  
This gives a visual impression of the content and quality of the dataset.

![Example](https://cdn-uploads.huggingface.co/production/uploads/634e9aa407e669188d3912f9/IGAJThvJXY8TsFRY4ja75.png)

---

## 📦 Dataset Overview

This dataset contains **125 high-resolution (HR) images** curated for validating and benchmarking SISR models.  
It is primarily intended for computing evaluation metrics during model training or cross-model comparison.

- All images originate from the **UHD-IQA** dataset (validation & test subsets) under the **CC0 1.0 Universal license**.  
- Images were downscaled (and cropped where necessary) to 512×512 using the **DPID** method via [pepedpid](https://github.com/umzi2/pepedpid) with λ ≈ 1.0 to preserve fine detail and sharpness.  
- The dataset includes corresponding **x2** and **x4** low-resolution subsets for use as inputs to SISR models.  

---

## 🧪 Validation Purpose

This dataset serves as a **public benchmark for SISR model validation**.  
It has been used for internal validation of the `2xPublic_realplksr_dysample_layernorm` model family — see  
👉 [github.com/Phhofm/models](https://github.com/Phhofm/models)

---

## 🔍 Quality Metric Criteria

Each image was evaluated using a set of **objective quality metrics** to ensure high perceptual fidelity and naturalness.  
Images failing to meet any single threshold were excluded, except for a few minor edge cases described below.

```python
# Default quality thresholds – tuned for SISR validation
DEFAULT_THRESHOLDS = {
    'brisque': 20,          # Pixel-level artifacts (<=)
    'hyperiqa': 0.7,        # Perceptual quality (>=)
    'maniqa': 0.6,          # Holistic quality (>=)
    'clipiqa_plus': 0.38,   # Semantic plausibility (>=)
    'topiq_nr': 0.6,        # Naturalness (>=)
    'musiq': 65,            # Multi-scale quality (>=)
    'tres': 80,             # Transformer-based quality (>=)
    'arniqa': 0.65,         # Agnostic quality (>=)
    'blockiness': 10,       # Compression artifacts (<)
    'complexity': 0.2       # Texture detail (>=)
}
````

The evaluation script is optimized to compute faster metrics first, quickly rejecting low-quality samples.

---

## 📈 Dataset Composition & Deviations

* **Total images:** 125
* **Strictly pass all thresholds:** ~121
* **Minor deviations:** ~4 (included for diversity and edge-case coverage)

For transparency, the **full IQA metric scores** for every image are provided in the included
[`scores.csv`](https://huggingface.co/datasets/Phips/PSISRD_val125/resolve/main/score.csv) file, along with pass/fail flags for each metric.

---

## ⚠️ Notes on Deviations

A few samples slightly deviate from the strict metric thresholds but remain **visually excellent** and valuable for validation:

| File   | Metric  | Value | Threshold | Deviation |
| ------ | ------- | ----- | --------- | --------- |
| 2.png  | brisque | 20.53 | ≤ 20      | +0.53     |
| 13.png | brisque | 20.27 | ≤ 20      | +0.27     |
| 31.png | maniqa  | 0.599 | ≥ 0.60    | -0.001    |
| 69.png | —       | —     | —         | —         |

These outliers provide useful edge cases for SISR model validation.

---

## 📄 License

All images are distributed under the **Creative Commons Zero (CC0 1.0 Universal)** license.
✅ Free for any purpose — research, commercial, or derivative use — **no attribution required**.

---

## 🔗 Related Resources

* **Training Dataset:** PSISRD — currently **13,761 images (512×512)** curated and filtered the same way as PSISRD_VAL125.
  Fully open under **CC0**, and can be used freely for training SISR models.
* **Models:** `2xPublic_realplksr_dysample_layernorm` — trained on PSISRD, validated on PSISRD_VAL125 → [GitHub repo](https://github.com/Phhofm/models)
* **Downscaling Method:** [pepedpid](https://github.com/umzi2/pepedpid) (DPID implementation)