Piq

PyTorch Image Quality (PIQ) v0.5.2 Release Notes
- New Feature: Haar Wavelet-Based Perceptual Similarity Index (164)
- Documentation (167, 174 )
- Enhancements (156, 158, 162, 175, 171)

New Features
Haar Wavelet-Based Perceptual Similarity Index (HaarPSI) (164)

Documentation
- Fixed the description of the arguments of `LPIPS` and `DISTS` (167)
- Updated README (174)


Enhancements
- Got rid of redundant warning checking the type of the input tensor (156)
- Made testing 10 times faster after update of `scipy` version to use (158)
- Added the missing parameter to the GMSD, MS-GMSD and MS-GMSDc metrics (162)
- Added `no_grad` context for computing features, where is no need in gradients (175)
- Fixed logic of the `GMSD` test for different pixel value ranges (171)

**Contributors:** zakajd, PavelOstyakov, snk4tr, denproc, .

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](https://arxiv.org/pdf/1608.07433.pdf)). The proposed implementation is similar to [original MATLAB implementation](https://www.mathworks.com/matlabcentral/fileexchange/59809) and supports the same functionality. Check [README.md](https://github.com/photosynthesis-team/piq#examples) 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](https://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm). Visit [README.md](https://github.com/photosynthesis-team/piq#examples) for new usage examples.

Project Infrastructure

Package Deployment at Anaconda Cloud (138)
The PIQ framework was added to [Anaconda Cloud](https://anaconda.org/photosynthesis-team/piq) at [photosynthesis-team](https://anaconda.org/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](https://github.com/photosynthesis-team/piq#installation).

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](https://github.com/photosynthesis-team/piq#examples).

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.

PyTorch Image Quality (PIQ) v0.5.0 Release Notes
- New feature: Perceptual losses (StyleLoss, ContentLoss , LPIPS, DISTS)
- New feature: Visual Saliency-induced Index (VSI)
- New feature: Feature Similarity (FSIM/FSIMc)
- New feature: `piq.functional`
- Code quality assessment
- BugFix

New Features
Perceptual losses (StyleLoss, ContentLoss , LPIPS and DISTS) (96, 131)
[Content loss](https://arxiv.org/abs/1508.06576), [Style loss](https://arxiv.org/abs/1508.06576), [Learned Perceptual Image Patch Similarity (LPIPS)](http://arxiv.org/abs/1801.03924) and [Deep Image Structure and Texture Similarity (DISTS)](https://arxiv.org/abs/2004.07728) metrics were added to the library. One could choose the extractor VGG16 or VGG19. Any number of layers is supported. Any weights are supported. One can use per-level (Style transfer case) or per-feature (as in LPIPS) weights. Check out [Examples](https://github.com/photosynthesis-team/piq/tree/v0.5.0#examples) at README.md for more details.

Visual Saliency-induced Index (VSI) (119)
The [Visual Saliency-induced Index (VSI)](https://ieeexplore.ieee.org/document/6873260) was introduced into the library in this release. The VSI is compatible with greyscale and RGB images. In case of greyscale, the only channel is copied to imitate RGB colour space. The VSI was compared with the original [MATLAB implementation](http://sse.tongji.edu.cn/linzhang/IQA/VSI/VSI.htm). Tests show that our implementation produces the same result. Check out [Examples](https://github.com/photosynthesis-team/piq/tree/v0.5.0#examples) at README.md for more details.

Feature Similarity (FSIM/FSIMc) (104)
The [Feature Similarity (FSIM/FSIMc)](https://www4.comp.polyu.edu.hk/~cslzhang/IQA/TIP_IQA_FSIM.pdf) was also included in this release. The FSIM measure is compatible with greyscale and RGB images, but only the luminance component is used. The FSIMc extension takes chrominance components into account. The FSIM and FSIMc were compared with the original [MATLAB implementation](https://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/Files/FeatureSIM.m). Tests show that our implementation produces the same result. Check out [Examples](https://github.com/photosynthesis-team/piq/tree/v0.5.0#examples) at README.md for more details.

`piq.functional` (104, 119, 135)
With this release, we introduce `piq.functional`, which contains the most common functions such as colour space conversions, filters, mesh grid, similarity and gradient maps. The decision was made in order to reduce copy-paste code and improve readability providing the user with the ability to reuse implemented functionality for their own needs.

Code quality assessment (99)
[CodeCov](https://codecov.io/gh/photosynthesis-team/piq) was introduced to the project to improve the quality of the contributed code to the library. The Codecov bot posts reports directly to pull requests highlighting the test coverage of the code.

BugFixes
GS (112)
The issue (111) with memory losses and the number of open files compatibility was resolved. The context manager was added to handle pool closure after its use.

BRISQUE Data Range (110)
The issue (108) with pixel values range to be used was resolved. The functionality is aligned with [MATLAB implementation](https://github.com/bukalapak/pybrisque).

VIF (94)
- Tests were added to cover bug behaviour;
- EPS constant was added before division to avoid NaN;
- Some checks were introduced from original MATLAB implementation that have been missed;
- Reduction parameter was added;
- Another small bug was fixed.

SonarCloud (117)
The code was refactored to meet the code style requirements and to remove sonarcloud code smells.


SSIM/MS-SSIM and BRISQUE Refactoring (134)
- The implementations of the SSIM/MS-SSIM and BRISQUE metrics were optimised for better readability and utility;
- The implementations were compared with available TensorFlow and MATLAB implementation on real images using new tests;
- Fixed huge memory consumption during SSIM/MS-SSIM testing (126);
- Other tests were refactored to improve readability and coverage. It allows flexible testing on all available devices (`cpu` and/or `gpu`) depending on the workstation for deployment.

**Contributors**: bonlime, denproc, zakajd, snk4tr.

PyTorch Image Quality (PIQ) v0.4.1 Release Notes
- New title
- New feature - PSNR
- Code quality assessment
- BugFix

New Title - PyTorch Image Quality (PIQ) (107)
New easy to remember title "PyTorch Image Quality (PIQ)" is introduced for our library with this release. This change allows importing all the metrics easier using just three letters `piq`. Moreover, the new title resolves the consistency between naming in different services (GitHub, PyPi). As a result, one could use automatic tools of IDE to detect if the library is messed and to install it straight away.

New Feature - Peak Signal-to-Noise Ratio (PSNR) (95)
The Peak Signal-to-Noise Ratio (PSNR) was added to the library in this release. The PSRN is compatible with greyscale and RGB images. In the case of an RGB image, one could use all three channels to compute the ratio or transfer image to YCbCr colour space and use luminance. Check out [Examples](https://github.com/photosynthesis-team/piq/tree/v0.4.1#examples) at README.md for more details.

Code quality assessment (99)
[SonarCloud](https://sonarcloud.io/dashboard?id=photosynthesis-team_photosynthesis.metrics) was introduced to the project to improve the quality of the contributed code to the library.

BugFixes
BRISQUE
- The issues with GPU compatibility were resolved (87);
- Back-propagation was fixed (88);
- New assertions were added for images with values of variance around zero (88).

MS-GMSD
- The issues with GPU compatibility were resolved (91).

FID
- The issues with GPU compatibility were resolved (103);
- The inconsistent result of the method due to inlace operations was fixed (103);
- The convergence issues of Newton-Schulz approach due to float precision were mended (103).

**Contributors**: denproc, zakajd, snk4tr.
Special thanks to PavelOstyakov for valuable feedback.

New Features
- Gradient magnitude similarity deviation (GMSD)(68)
- Multi scale gradient magnitude similarity deviation (MS-GMSD) (68)
- Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) (79)

Enhancement
- Update of Total Variation interface (78)
- Upgrade of input tensor validation to improve robustness (73, 78)
- Tests for utils (78)

Other
- Documentation updates (72, 75)

**Contributiors: denproc, snk4tr, zakajd**

New features

- Geometry Score (GS) (41)
- Support of SSIM and MS-SSIM for 5D tensors in the form that is used in the medical imaging (55)

Enchancements

- Total Variation (TV) now supports 2D, 3D and 4D tensors (55)
- MS-SSIM was upgraded to use custom scale weights of different size (55)

Other

Small fixes (62, 63, 65) and refactoring (64)

**Contributors**: denproc, zakajd, snk4tr