Scale-Agnostic Image Quality Assessment via Magnitude-Aware Ranking

Presented at QoMEX 2026
Adam Zizien, Karel Fliegel

Try a model of your choice in your browser (no install): https://zizieada.github.io/wphl/

This repository accompanies the paper Scale-Agnostic Image Quality Assessment via Magnitude-Aware Ranking. The core contribution is the Weighted Pairwise Hinge Loss (WPHL), which is a loss function that incorporates label magnitude into both the margin and the sample weighting. This enables the model to be sensitive not just to the direction of quality differences, but to their magnitude, without requiring score normalization across training sets.

The framework is evaluated on top of CKDN (the reimplementation provided in IQA-PyTorch), a knowledge-distillation-based IQA model, and the retrained weights are provided.

The weights are released in three formats: PyTorch checkpoints, TorchScript traces, and ONNX exports. The two ONNX models run directly in the browser through the hosted scorer at https://zizieada.github.io/wphl/, with no Python or install, and no image leaves your machine. See Pretrained Models below.

├── WeightedPairwiseHinge.py      # Proposed loss function (PyTorch)
├── train_example.py              # Minimal example: pairwise training with WPHL
├── index.html                    # Browser-based ONNX scorer (also hosted, link above)
├── Evaluation_results/
│   ├── README.md                 # Dataset disclosure and citation guidance
│   └── *.csv                     # Subjective scores + objective metric

The Weighted Pairwise Hinge Loss is implemented in WeightedPairwiseHinge.py.

import torch
from WeightedPairwiseHinge import WeightedPairwiseHinge

criterion = WeightedPairwiseHinge(margin=0.5, p=0.5)

# Predicted scores for a batch of image pairs
o1 = torch.tensor([0.8, 0.3, 0.6])
o2 = torch.tensor([0.5, 0.7, 0.4])

# Ground-truth quality labels (any scale — e.g. MOS)
y1 = torch.tensor([4.2, 2.1, 3.8])
y2 = torch.tensor([2.8, 3.9, 2.5])

# Ranking target: +1 if image 1 is better, -1 otherwise
target = torch.sign(y1 - y2)

loss, acc = criterion(o1, o2, y1, y2, target)

Retrained CKDN weights are available in the latest release, in three formats:

Both .onnx files run client-side in any modern browser, either through the hosted page at https://zizieada.github.io/wphl/ or by opening iqa_scorer.html from this repository locally. No Python, PyTorch, or install is required, and no image leaves your machine.

1. Load model — select CKDN_ImageNet.onnx or CKDN_WPHL+.onnx.
2. Choose reference image — the original / undistorted image.
3. Choose distorted image(s) — one or many versions to evaluate.
4. Compute scores, then Export CSV.

Both models take two inputs (dist, ref) in that order and emit a single scalar score per pair. Whether a higher or lower score means "better" depends on the model's training direction. The raw outputs are not calibrated to a fixed scale, so interpret them against your own validation set.

Note: each distorted image should depict the same content as the reference, or the score is meaningless. The first browser run fetches ONNX Runtime Web from a CDN; see the comments at the bottom of iqa_scorer.html for fully offline use.

import torch

model = torch.jit.load("CKDN_ImageNet_traced.pt", map_location="cpu")
model.eval()

train_example.py demonstrates how to integrate WPHL into a pairwise training loop with a generic IQA backbone. See the file for the full annotated example.

If you use the loss function, pretrained models, or evaluation data from this repository, please cite:

@inproceedings{zizien2026wphl,
  author    = {Zizien, Adam and Fliegel, Karel},
  booktitle = {2026 18th International Conference on Quality of Multimedia Experience (QoMEX)},
  date      = {2026-06},
  title     = {Scale-Agnostic Image Quality Assessment via Magnitude-Aware Ranking},
  doi       = {10.1109/qomex69967.2026.11618328},
  pages     = {1--7},
  publisher = {IEEE},
}

If you use the evaluation scores in Evaluation_results/, please also cite the original dataset authors as listed in Evaluation_results/README.md.

This repository (code, pretrained models, and evaluation data) is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material for any purpose, including commercial use, provided appropriate credit is given, a link to the license is included, and any changes are indicated.

Note that the underlying datasets referenced in Evaluation_results/ are the work of their respective authors and may carry separate license terms. Consult the original dataset papers before redistributing that data.