# SSIMULACRA

> perceptual image quality metric

**Wikidata**: [Q117789739](https://www.wikidata.org/wiki/Q117789739)  
**Source**: https://4ort.xyz/entity/ssimulacra

## Summary
SSIMULACRA is a perceptual image quality metric developed by Jon Sneyers, designed to evaluate how well an image preserves visual quality after compression or other processing.

## Key Facts
- SSIMULACRA is an objective quality metric and software developed by Jon Sneyers.
- It was created by Belgian software developer Jon Sneyers in 2017.
- It is licensed under the Apache Software License 2.0.
- It is a perceptual image quality metric that differs from structural similarity (SSIM).
- It was succeeded by SSIMULACRA 2, indicating a versioned series.
- Its publication date is June 13, 2017.
- The source code is available on GitHub at https://github.com/cloudinary/ssimulacra.
- It is copyrighted and classified as a software component.

### FAQs
### Q: What is SSIMULACRA used for?
A: It is a perceptual image quality metric used to evaluate how well an image retains visual quality after compression or processing.

### Q: Who developed SSIMULACRA?
A: It was developed by Jon Sneyers, a Belgian software developer and computer scientist.

### Q: How does SSIMULACRA differ from structural similarity (SSIM)?
A: It differs from SSIM by focusing on revealing local artifacts and compression-related issues rather than just structural similarity.

### Q: Is the source code for SSIMULACRA available?
A: Yes, the source code is available on GitHub under the Apache Software License 2.0.

## Why It Matters
SSIMULACRA addresses the limitations of traditional image quality metrics by focusing on perceptual quality, making it valuable for evaluating image compression, enhancement, and restoration. It helps developers and researchers assess how well images retain visual fidelity after processing, which is critical in fields like photography, video streaming, and medical imaging. By prioritizing human-perceived quality over structural similarity, it provides a more accurate measure of image quality for real-world applications.

## Notable For
- It is a perceptual image quality metric that reveals local artifacts and compression-related issues, distinguishing it from traditional metrics like structural similarity.
- It is open source, licensed under the Apache Software License 2.0, making its source code accessible to developers.
- It was developed by Jon Sneyers, a Belgian software developer, and is part of a series that evolved into SSIMULACRA 2.
- It addresses the limitations of prior metrics by focusing on human-perceived quality rather than just structural similarity.

## Body
### Overview
SSIMULACRA is defined as a perceptual image quality metric, meaning it evaluates how well an image preserves visual quality after compression or other processing. Its primary purpose is to provide a more accurate measure of image fidelity that aligns with human perception.

### Development and Publication
SSIMULACRA was created by Jon Sneyers, a Belgian software developer and computer scientist. He developed it in 2017, as indicated by the publication date of June 13, 2017. The source code for SSIMULACRA is publicly available on GitHub, with the repository URL being https://github.com/cloudinary/ssimulacra.

### Technical Characteristics
As an objective quality metric, SSIMULACRA is classified as software. It is licensed under the Apache Software License 2.0, which allows for open use and modification. A key differentiator is that it is not the same as structural similarity (SSIM); instead, it focuses on revealing local artifacts and compression-related issues that traditional metrics may miss.

### Evolution
SSIMULACRA was succeeded by SSIMULACRA 2, indicating a versioned series that likely builds upon or refines the original metric. This evolution suggests ongoing development to improve its capabilities.

### Usage and Impact
SSIMULACRA is used to assess image fidelity in tasks such as image compression, enhancement, and restoration. Its focus on perceptual quality makes it valuable for applications where human visual perception is critical, such as in photography, video streaming, and medical imaging. By providing a more accurate measure of image quality, it helps developers optimize their image processing pipelines to deliver better results.

```json
{
  "@context": "https://schema.org",
  "@type": "Thing",
  "name": "SSIMULACRA",
  "description": "A perceptual image quality metric",
  "url": "https://github.com/cloudinary/ssimulacra",
  "sameAs": ["https://github.com/cloudinary/ssimulacra"]
}

## References

1. [Source](https://github.com/cloudinary/ssimulacra/blob/master/LICENSE)
2. [Source](https://github.com/cloudinary/ssimulacra/commit/88d5a0fde4a17d641e75ff1328abbe55b5e8fa44)