This is the original SSIMULACRA metric from 2016-2017. It will no longer be updated; however, there is a new SSIMULACRA 2 metric available at https://github.com/cloudinary/ssimulacra2 which correlates better with human opinions.

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 https://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)

Known limitations:

• 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
• Do make

May 2016 - Feb 2017, Jon Sneyers jon@cloudinary.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

https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.