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 |
|
| DISTS | dists |
|
| WaDIQaM | No pretrain models | |
| 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 |
|---|---|---|
| TOPIQ | topiq_nr, topiq_nr-flive, topiq_nr-spaq |
TOPIQ with different datasets, koniq by default |
| TReS | tres, tres-koniq, 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-koniq, maniqa-pipal |
MUSIQ with different datasets, koniq by default |
| MUSIQ | musiq, musiq-koniq, 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 | No pretrain models | |
| CNNIQA | cnniqa |
|
| NRQM(Ma)2 | nrqm |
No backward |
| PI(Perceptual Index) | pi |
No backward |
| BRISQUE | brisque |
No backward |
| ILNIQE | ilniqe |
No backward |
| NIQE | niqe |
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 |
|---|---|---|
| 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 |
Outputs of Different Metrics
Note: ~ means that the corresponding numeric bound is typical value and not mathematically guaranteed
| model | lower better ? | min | max | DATE | Link |
|---|---|---|---|---|---|
| clipiqa | False | 0 | 1 | 2022 | https://arxiv.org/abs/2207.12396 |
| maniqa | False | 0 | 2022 | https://arxiv.org/abs/2204.08958 | |
| hyperiqa | False | 0 | 1 | 2020 | |
| cnniqa | False | 2014 | |||
| tres | False | 2022 | https://github.com/isalirezag/TReS | ||
| musiq | False | ~0 | ~100 | 2021 | https://arxiv.org/abs/2108.05997 |
| musiq-ava | False | ~0 | ~10 | 2021 | https://arxiv.org/abs/2108.05997 |
| musiq-koniq | False | ~0 | ~100 | 2021 | https://arxiv.org/abs/2108.05997 |
| musiq | False | 2021 | https://arxiv.org/abs/2108.05997 | ||
| paq2piq | False | 2020 | |||
| dbcnn | False | 2019 | https://arxiv.org/bas/1907.02665 | ||
| brisque | True | 2012 | |||
| pi | True | 2018 | https://arxiv.org/abs/1809.07517 | ||
| nima | False | 2018 | https://arxiv.org/abs/1709.05424 | ||
| nrqm | False | 2016 | https://arxiv.org/abs/1612.05890 | ||
| ilniqe | True | 0 | 2015 | ||
| niqe | True | 0 | 2012 |