SR-SIM, DSS, PIPAL Benchmark and Enhancements
PyTorch Image Quality (PIQ) v0.6.0 Release Notes
- Spectral Residual based Similarity (SR-SIM, SR-SIMc) Metric (#202)
- DCT Subbands Similarity (DSS) Metric (#225, #268)
- Benchmark on PIPAL dataset (#269)
New Features
Spectral Residual based Similarity (#202)
With current release, we added Spectral Residual based Similarity (SR-SIM) measure. The metric was introduced based on a specific visual saliency model, spectral residual visual saliency.
In addition, we also implemented SR-SIMc, which is a chromatic version of the SR-SIM.
DCT Subbands Similarity (DSS) (#225, #268)
DCT Subbands Similarity (DSS) was presented visual quality metric that correlates well with human visual perception. The measure uses properties of human visual perception, evaluating changes in structural information in sub-bands in DCT domain. DSS showed great results according to public image datasets benchmarks, while being computationally efficient.
PIPAL Benchmark (#269)
In this release we added another public image dataset benchmark. PIPAL is the largest human-rated set of images to date and the only one containing rich number of realistic distortions from GAN models. Benchmarking metrics performance on this set can give a good estimate of their usefulness in GAN modes evaluation.
Benchmark results are available at README.rst and documentatioin.
