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 (BIQSAA) method by considering the wavelet transform as a linear operation that decomposes a complex signal into elementary blocks at different scales or resolutions. BIQSAA decomposes a distorted image into a set of wavelet planes ω λ, ϕ of different spatial frequencies λ and spatial orientations ϕ, and it transforms these wavelet planes into one-dimension vector Ω using a Hilbert scanning. From the vector Ω 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 Lena at 24bpp, at 1.375bpp, and at 0.50bpp were H 24b p p = 0.0395, H 1.375b p p = 0.0551, and H 0.50b p p = 0.0612, respectively. Our experiments show that BIQSAA algorithm improves in 14.36% the Human Visual System correlation, respect to the four state-of-the-art No-Reference Image Quality Assessments.

Fractal Characterization of Stochastic Series Fluctuations of Children with Reading Disorders.

Reading is an emerging process from human brain activity. This process sometimes is subject to disorders which has been studied from the performance of studies that provide data that are treated with qualitative and quantitative linear tools to obtain the average behavior determined and the causality of it. This research focuses on the nonlinear quantitative study of reading disorder and in this way fractal geometry and roughness interface growth theory approach were selected to be used in the processing of brain wave quantification (EEG). From the EEG of children with and without reading disorders in the State of Mexico (experimental and control group) were built time series of standard deviation for each of the 19 channels distributed in cerebral cortex. The self-affinity of these time series (treated as interfaces in motion) is studied by the scaling behavior of their structure functions. It was found that the behavior of the time series of children with reading problems (experimental group) and without them (control group) is similar to the Family-Vicsek scaling dynamic for a kinetic roughening of moving interface.