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

作者：Bampis Christos G^*; Gupta Praful; Soundararajan Rajiv; Bovik Alan C

来源：IEEE Signal Processing Letters, 2017, 24(9): 1333-1337.

DOI：10.1109/LSP.2017.2726542

• Entropic differencing
• image and video quality assessment (I/VQA)
• reduced-reference (RR) models

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