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MDSI, GMSD interfaces, Anaconda Cloud Deployment, CI and Bugfix
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@denproc denproc released this 28 Jul 07:52
· 85 commits to master since this release
v0.5.1
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PyTorch Image Quality (PIQ) v0.5.1 Release Notes

  • New Feature: Mean Deviation Similarity Index (MDSI) (#148)
  • New Features: Functional interface for Gradient Magnitude Similarity Deviation (GMSD) and Multi-Scale Gradient Magnitude Similarity Deviation (MS-GMSD) (#151)
  • Project Infrastructure: Package Deployment to Anaconda Cloud (#138)
  • Project Infrastructure: Optimisation of CI workflows (#153)
  • Bugfix (#140, #144, #146, #148, #154)

New Features

Mean Deviation Similarity Index (MDSI) (#148)

With this release we introduce Mean Deviation Similarity Index (MDSI). The proposed implementation is similar to original MATLAB implementation and supports the same functionality. Check README.md for usage examples.

Functional interface for Gradient Magnitude Similarity Deviation (GMSD) and Multi-Scale Gradient Magnitude Similarity Deviation (MS-GMSD) (#151)

Now, the functional interfaces for GMSD and Multi-Scale GMSD are available for the users. The precision was improved for each metric making GMSD prediction fully aligned with the original MATLAB version. Visit README.md for new usage examples.

Project Infrastructure

Package Deployment at Anaconda Cloud (#138)

The PIQ framework was added to Anaconda Cloud at photosynthesis-team channel. The proposed CD pipeline allows deploying the latest release to the Anaconda Cloud automatically. The library is compatible with win-64, linux-64 and osx-64 and python>=3.6. For installation tips, visit the README.md.

Optimisation of CI workflows (#153)

The testing CI was extended with validation using python 3.8 resulting in the library tested for python 3.6, 3.7, 3.8. The flake8 CI validation using python 3.7 was deprecated because it duplicates the functionality of the same validation using python 3.6.

Bugfix

Fixed Import of Feature Encoders (#140)

Added the description of the feature extractors into piq.feature_extractors.__init__.py for more convenient user experience.

Description of the LPIPS, DISTS, Content loss and Style loss (#144)

Added the description of the LPIPS, DISTS, ContentLoss and StyleLoss and their usage to the README.md.

Add chromatic parameter to FSIM loss (#146)

Added the chromatic parameter of the FSIMLoss to take into account chromatic components computing the FSIM.

Minor enhancements (#148)

  • Added the validation of the kernel_size to be odd in brisque function;
  • Added pow_for_complex to calculate tensors of any values (real and complex) in the power of any real number;
  • Changed the padding for MS-SSIM to be similar to TensorFlow version;
  • Updated docstring to the same format (tabulation).

Less strict requirements (#154)

The requirements were made less strict to avoid force update of the environment with torch for incompatible CUDA support during piq installation. The brisque measure was updated with a warning that back propagation is not available for torch==1.5.0 due to bug in argmin and argmax. The same warning was added to README.md.

Contributors: @denproc, @zakajd, @snk4tr.

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