SpEED-QA: Spatial Efficient Entropic Differencing for Image and Video Quality
Abstract
Many image and video quality assessment (I/VQA) models rely on data transformations of image/video frames, which increases their programming and computational complexity. By comparison, some of the most popular I/VQA models deploy simple spatial bandpass operations at a couple of scales, making them attractive for efficient implementation. Here we design reduced-reference image and video quality models of this type that are derived from the high-performance reduced reference entropic differencing (RRED) I/VQA models. A new family of I/VQA models, which we call the spatial efficient entropic differencing for quality assessment (SpEED-QA) model, relies on local spatial operations on image frames and frame differences to compute perceptually relevant image/video quality features in an efficient way. Software for SpEED-QA is available at: http://live.ece.utexas.edu/research/Quality/SpEED_Demo.zip.
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- Authors
- Bampis, C. G., Gupta, P., Soundararajan, R., & Bovik, A. C.
- Affiliations
- The University of Texas at Austin (US)
- Indian Institute of Science Bangalore (IN)
- Venue
- IEEE Signal Processing Letters
- Published
- 2017-09
- Volume
- 24
- Issue
- 9
- Pages
- 1333-1337
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2735448770
- Semantic Scholar
- 7456d4e7f73994efae5148ba6617684696b07b31
- Cited by
- 150
- References
- 42
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 15 · 2023: 21 · 2022: 22 · 2021: 25 · 2020: 19 · 2019: 13 · 2018: 16
- FWCI
- 5.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Signal Processing Letters (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- image quality assessment, video quality assessment, frame difference, efficient implementation
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