List all model names with:
import pyiqa
print(pyiqa.list_models())
| FR Method | Model names | Description |
|---|---|---|
| TOPIQ | topiq_fr, topiq_fr-pipal |
Proposed in this paper |
| AHIQ | ahiq |
|
| PieAPP | pieapp |
|
| LPIPS | lpips, lpips-vgg, stlpips, stlpips-vgg, lpips+, lpips-vgg+ |
|
| DISTS | dists |
|
| WaDIQaM | wadiqam_fr |
|
| CKDN1 | ckdn |
|
| FSIM | fsim |
|
| SSIM | ssim, ssimc |
Gray input (y channel), color input |
| MS-SSIM | ms_ssim |
|
| CW-SSIM | cw_ssim |
|
| PSNR | psnr, psnry |
Color input, gray input (y channel) |
| VIF | vif |
|
| GMSD | gmsd |
|
| NLPD | nlpd |
|
| VSI | vsi |
|
| MAD | mad |
| NR Method | Model names | Description |
|---|---|---|
| Q-Align | qalign (with quality[default], aesthetic options) |
Large vision-language models |
| LIQE | liqe, liqe_mix |
CLIP based method |
| ARNIQA | arniqa, arniqa-live, arniqa-csiq, arniqa-tid, arniqa-kadid, arniqa-clive, arniqa-flive, arniqa-spaq |
ARNIQA with different datasets, koniq by default |
| TOPIQ | topiq_nr, topiq_nr-flive, topiq_nr-spaq |
TOPIQ with different datasets, koniq by default |
| TReS | tres, tres-flive |
TReS with different datasets, koniq by default |
| FID | fid |
Statistic distance between two datasets |
| CLIPIQA(+) | clipiqa, clipiqa+, clipiqa+_vitL14_512,clipiqa+_rn50_512 |
CLIPIQA(+) with different backbone, RN50 by default |
| MANIQA | maniqa, maniqa-kadid, maniqa-pipal |
MUSIQ with different datasets, koniq by default |
| MUSIQ | musiq, musiq-spaq, musiq-paq2piq, musiq-ava |
MUSIQ with different datasets, koniq by default |
| DBCNN | dbcnn |
|
| PaQ-2-PiQ | paq2piq |
|
| HyperIQA | hyperiqa |
|
| NIMA | nima, nima-vgg16-ava |
Aesthetic metric trained with AVA dataset |
| WaDIQaM | wadiqam_nr |
|
| CNNIQA | cnniqa |
|
| NRQM(Ma)2 | nrqm |
No backward |
| PI(Perceptual Index) | pi |
No backward |
| BRISQUE | brisque, brisque_matlab |
No backward |
| ILNIQE | ilniqe |
No backward |
| NIQE | niqe, niqe_matlab |
No backward |
| PIQE | piqe |
No backward |
[1] This method use distorted image as reference. Please refer to the paper for details.
[2] Currently, only naive random forest regression is implemented and does not support backward.
| Task | Method | Description |
|---|---|---|
| Color IQA | msswd |
Perceptual color difference metric MS-SWD, ECCV2024, Arxiv, Github |
| Face IQA | topiq_nr-face |
TOPIQ model trained with face IQA dataset (GFIQA) |
| Underwater IQA | uranker |
A ranking-based underwater image quality assessment (UIQA) method, AAAI2023, Arxiv, Github |
Note: ~ means that the corresponding numeric bound is typical value and not mathematically guaranteed
You can now access the rough output range of each metric like this:
metric = pyiqa.create_metric('lpips')
print(metric.score_range)