Cloudinary's variant of DSSIM, based on Philipp Klaus Krause's adaptation of Rabah Mehdi's SSIM implementation, using ideas from Kornel Lesinski's DSSIM implementation as well as several new ideas.
This is a perceptual metric designed specifically for scoring image compression related artifacts in a way that correlates well with human opinions. For more information, see http://cloudinary.com/blog/detecting_the_psychovisual_impact_of_compression_related_artifacts_using_ssimulacra
Changes compared to Krause's SSIM implementation:
- Use C++ OpenCV API
- Convert sRGB to linear RGB and then to Lab*, to get a perceptually more accurate color space
- Multi-scale (6 scales)
- Extra penalty for specific kinds of artifacts:
- local artifacts
- grid-like artifacts (blockiness)
- introducing edges where the original is smooth (blockiness / color banding / ringing / mosquito noise)
- Color profiles are ignored; input images are assumed to be sRGB.
- Both input images need to have the same number of channels (Grayscale / RGB / RGBA)
- Install OpenCV
May 2016 - Feb 2017, Jon Sneyers email@example.com
Copyright 2017, Cloudinary
SSIMULACRA is licensed under the Apache License, Version 2.0 (the "License"). You may obtain a copy of the License at
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