Defining a no-reference image quality assessment by means of the self-affine analysis.

In this paper we propose a novel Blind Image Quality Assessment via Self-Affine Analysis () method by considering the wavelet transform as a linear operation that decomposes a complex signal into elementary blocks at different scales or resolutions. decomposes a distorted image into a set of wavelet planes of different spatial frequencies and spatial orientations , and it transforms these wavelet planes into one-dimension vector using a Hilbert scanning. From the vector there were obtained their wavelet coefficient fluctuations estimated by the inverse of the Hurst exponent in decibels, whose scaling-law or fractal behavior was obtained by applying Fractal Geometry or Self-Affine Analysis. The scaling exponents calculated for the coefficient fluctuation behavior of Image at 24bpp, at 1.375bpp, and at 0.50bpp were = 0.0395, = 0.0551, and = 0.0612, respectively. Our experiments show that algorithm improves in 14.36 the Human Visual System correlation, respect to the four state-of-the-art No-Reference Image Quality Assessments.