# A Hitchhiker's Guide to Structural Similarity

arXiv: https://arxiv.org/abs/2101.06354 · HTML: https://mlsanity.com/paper/2101.06354/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 72 references that are in the map.
- First posted on arXiv 16 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator](https://mlsanity.com/paper/1608.07433/) (2016)
- [Daala: A Perceptually-Driven Next Generation Video Codec](https://mlsanity.com/paper/1603.03129/) (2016)

## Built on by (most cited first, 16 of 16)

- [A study of why we need to reassess full reference image quality assessment with medical images](https://mlsanity.com/paper/2405.19097/) (2024)
- [DSSIM: a structural similarity index for floating-point data](https://mlsanity.com/paper/2202.02616/) (2022)
- [A study on the adequacy of common IQA measures for medical images](https://mlsanity.com/paper/2405.19224/) (2024)
- [FUNQUE: Fusion of Unified Quality Evaluators](https://mlsanity.com/paper/2202.11241/) (2022)
- [Are Synthetic Time-series Data Really not as Good as Real Data?](https://mlsanity.com/paper/2402.00607/) (2024)
- [A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos](https://mlsanity.com/paper/2312.08524/) (2023)
- [Step Saver: Predicting Minimum Denoising Steps for Diffusion Model Image Generation](https://mlsanity.com/paper/2408.02054/) (2024)
- [Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Content](https://mlsanity.com/paper/2404.13484/) (2024)
- [Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions](https://mlsanity.com/paper/2404.19015/) (2024)
- [HistDiT: A Structure-Aware Latent Conditional Diffusion Model for High-Fidelity Virtual Staining in Histopathology](https://mlsanity.com/paper/2604.08305/) (2026)
- [Edge-Aware Image Color Appearance and Difference Modeling](https://mlsanity.com/paper/2304.10669/) (2023)
- [Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos](https://mlsanity.com/paper/2404.13452/) (2024)
- [Dual Prompting for Diverse Count-level PET Denoising](https://mlsanity.com/paper/2505.03037/) (2025)
- [AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters](https://mlsanity.com/paper/2605.12937/) (2026)
- [Spatially Localized Image Degradation Embeddings for Image Quality Assessment](https://mlsanity.com/paper/2606.29162/) (2026)
- [Local SVD-Entropy Maps as a Complementary Structural Representation for Full-Reference and No-Reference Image Quality Assessment](https://mlsanity.com/paper/2609.27959/) (2026)

## Abstract (arXiv)

The Structural Similarity (SSIM) Index is a very widely used image/video quality model that continues to play an important role in the perceptual evaluation of compression algorithms, encoding recipes and numerous other image/video processing algorithms. Several public implementations of the SSIM and Multiscale-SSIM (MS-SSIM) algorithms have been developed, which differ in efficiency and performance. This "bendable ruler" makes the process of quality assessment of encoding algorithms unreliable. To address this situation, we studied and compared the functions and performances of popular and widely used implementations of SSIM, and we also considered a variety of design choices. Based on our studies and experiments, we have arrived at a collection of recommendations on how to use SSIM most effectively, including ways to reduce its computational burden.