SpEED-QA: Spatial Efficient Entropic Differencing for Image and Video Quality

2017/07/13 by Christos G. Bampis, Praful Gupta, Rajiv Soundararajan +1 · 1 citation
Computer Science · Engineering · #Advanced Image Fusion Techniques #Image and Signal Denoising Methods #Image and Video Quality Assessment

paper · doi:10.1109/lsp.2017.2726542

openalex publication_date 2017/07/13 · crossref created 2017/07/13 · crossref issued 2017/09/01 · crossref published 2017/09/01 · crossref published-print 2017/09/01 · crossref deposited 2022/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/11 · crossref indexed 2026/07/30

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.

Citations

• A Feature-Enriched Completely Blind Image Quality Evaluator
• No-Reference Image Quality Assessment in the Spatial Domain
• Image information and visual quality
• Making a “Completely Blind” Image Quality Analyzer
• Study of Subjective and Objective Quality Assessment of Video
• Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
• Screenings in the rain
• A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms

Cited by

• Predicting Encoded Picture Quality in Two Steps is a Better Way

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