Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2101.06354 (eess)
[Submitted on 16 Jan 2021 (v1), last revised 30 Jan 2021 (this version, v2)]

Title:A Hitchhiker's Guide to Structural Similarity

Authors:Abhinau K. Venkataramanan, Chengyang Wu, Alan C. Bovik, Ioannis Katsavounidis, Zafar Shahid
View PDF
Abstract: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.
Comments: Submitted final version to IEEE Access on January 30, 2021
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
Cite as: arXiv:2101.06354 [eess.IV]
  (or arXiv:2101.06354v2 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2101.06354
Related DOI: https://doi.org/10.1109/ACCESS.2021.3056504

Submission history

From: Abhinau Venkataramanan [view email]
[v1] Sat, 16 Jan 2021 02:51:06 UTC (3,602 KB)
[v2] Sat, 30 Jan 2021 23:40:28 UTC (4,682 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
eess.IV
< prev   |   next >
new | recent | 2021-01
Change to browse by:
cs
cs.CV
cs.MM
eess

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)