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