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Model Cards for IQA-PyTorch

General FR/NR Methods

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.

IQA Methods for Specific Tasks

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

Metric Output Score Range

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)