IQA-PyTorch v0.1.5

⚠️ Fix bugs

1. Fix FID bug
2. Fix read meta info error in livechallenge.
3. Fix shape error for NRQM
4. Fix bug in nancov
5. Add missing requirements package
6. Fix link for lpips squeeze net version

New features

1. Add MANIQA, AHIQ pretrained weights
2. Add metric_mode option for list_models
3. Add new metrics: FID, MANIQA
4. Enable image path as inputs. See demo codes in README
5. Add as_loss option to enable gradient backpropagation for metric. Default False.

Improvements

1. Use epoch instead of iteration in lr scheduler
2. Add clean_state_dict before loading pretrain model

IQA-PyTorch v0.1.4

New features

1. Add new metrics: FID, MANIQA
2. Enable image path as inputs. See demo codes in README
3. Add as_loss option to enable gradient backpropagation for metric. Default False.

Fix bugs

1. Fix rmse error
2. Fix benchmark test with PieAPP

Improvements

1. Disable gradient calculation by default for convenience.
2. Add filter2 function to matlab utils
3. Add reduction option to EMDLoss
4. Add crop_border option to PSNR, SSIM

pyiqa v0.1.3 beta version

New features

1. Add RMSE metric
2. Add scale fitting option for calculation of PLCC and RMSE

Fix bugs

1. Fix NIQE error when calculating images with large (>96 x 96) plain regions (regions with constant value). See #23
2. Correct batch inference error for pieapp
3. Fix compatibility of "torch.linalg.svd" for pytorch 1.9 #25

Improvements

1. Improve function interface to match original matlab codes, including nanmean, nancov, blockproc, fspecial.
2. Improve efficiency of symmetric padding, according to this link
3. For pieapp, we change default stride to 27 for computation-performance trade off.

IQA-PyTorch v0.1.3 Alpha version

New features

1. We add the following new metrics:
   • pieapp
   • paq2piq
   • dbcnn trained with our own splits and configurations
2. Add SRCC based loss function

Important change

We change the default musiq weights from musiq-ava to musiq-koniq because it is more robust according to NR benchmark results

Fix bugs

• Remove Lambda transform in dataset to enable distributed training
• Fix paq2piq batch test error

IQA-PyTorch v0.1.2 Alpha version

Important Change

• Change default color space from YCbCr to YIQ

New Features

• Add NRQM, PI, ILNIQE metrics.
• Add NIMA model trained on AVA
• Add lower_better flag. This indicates whether a lower metric score is better.

IQA-PyTorch v0.1.1 Alpha version

Bug fix

• Fix bugs in rgb2ycbcr

New Features

• Use round in to_y_channel for more consistent results with matlab
• Add NRQM metric

IQA-PyTorch v0.1.0 Alpha version

First experimental release version of pyiqa tools 😃 . It supports

• Installation with pip install pyiqa
• Several IQA metrics implemented with pure PyTorch. List supported metrics with pyiqa.list_models()

Hope this will help your research and project. We will add more features and pretrained models.
And welcome contribute, and report bugs ! 🍻

Pretrained Models Download

This release contains

• All model parameters and weights from official implementations.
• Data info files, including
  • .csv files: meta information of different datasets
  • .pkl files: train/split of different datasets