Image utility estimation using difference-of-Gaussian scale space

Traditional quality estimators evaluate an image's resemblance to a reference image. However, quality estimators are not well suited to the similar but somewhat different task of utility estimation, where an image is judged instead by how useful it would be in comparison to a reference in the context of accomplishing some task. Multi-Scale Difference of Gaussian Utility (MS-DGU), a reduced-reference algorithm for image utility estimation, relies on matching image contours across scales tuned to spatial frequencies important for utility estimation. MS-DGU estimates utility with greater accuracy than previous techniques. A fast algorithm for utility-optimized image compression was developed through rate-utility optimization for MS-DGU. By simple scaling of JPEG quantization step sizes according to a “utility factor,” data rates were reduced by an average of 24% (and up to 30%) compared to standard JPEG while maintaining utility.