Relationship between Fractal Dimension and Spectral Scaling Decay Rate in Computer-Generated Fractals

Two measures are commonly used to describe scale-invariant complexity in images: fractal dimension (D) and power spectrum decay rate (β). Although a relationship between these measures has been derived mathematically, empirical validation across measurements is lacking. Here, we determine the relationship between D and β for 1- and 2-dimensional fractals. We find that for 1-dimensional fractals, measurements of D and β obey the derived relationship. Similarly, in 2-dimensional fractals, measurements along any straight-line path across the fractal’s surface obey the mathematically derived relationship. However, the standard approach of vision researchers is to measure β of the surface after 2-dimensional Fourier decomposition rather than along a straight-line path. This surface technique provides measurements of β that do not obey the mathematically derived relationship with D. Instead, this method produces values of β that imply that the fractal’s surface is much smoother than the measurements along the straight lines indicate. To facilitate communication across disciplines, we provide empirically derived equations for relating each measure of β to D. Finally, we discuss implications for future research on topics including stress reduction and the perception of motion in the context of a generalized equation relating β to D.

[1]  Joachim Denzler,et al.  1/f2 Characteristics and Isotropy in the Fourier Power Spectra of Visual Art, Cartoons, Comics, Mangas, and Different Categories of Photographs , 2010, PloS one.

[2]  David J Field,et al.  Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities. , 2007, Spatial vision.

[3]  D. Marković,et al.  Power laws and Self-Organized Criticality in Theory and Nature , 2013, 1310.5527.

[4]  Christopher P Benton,et al.  Fractal rotation isolates mechanisms for form-dependent motion in human vision , 2007, Biology Letters.

[5]  D. Tolhurst,et al.  Amplitude spectra of natural images , 1992 .

[6]  Harsh Mathur,et al.  Fractal Analysis: Revisiting Pollock's drip paintings , 2006, Nature.

[7]  R. P. Taylor,et al.  Fractal Analysis: Revisiting Pollock's drip paintings (Reply) , 2006, Nature.

[8]  C. Cela-Conde,et al.  Predicting beauty: fractal dimension and visual complexity in art. , 2011, British journal of psychology.

[9]  Terry Purcell,et al.  Fractal dimension of landscape silhouette outlines as a predictor of landscape preference , 2004 .

[10]  William Bialek,et al.  Statistics of Natural Images: Scaling in the Woods , 1993, NIPS.

[11]  Thorbjörn Laike,et al.  Investigations of Human EEG Response to Viewing Fractal Patterns , 2008, Perception.

[12]  Daniel A. Lidar,et al.  The Limited Scaling Range of Empirical Fractals , 1998 .

[13]  V. Kiselev,et al.  A higher order visual neuron tuned to the spatial amplitude spectra of natural scenes , 2015, Nature Communications.

[14]  C. Redies,et al.  Statistical regularities in art: Relations with visual coding and perception , 2010, Vision Research.

[15]  J. Cutting,et al.  Fractal curves and complexity , 1987, Perception & psychophysics.

[16]  Joachim Denzler,et al.  From regular text to artistic writing and artworks: Fourier statistics of images with low and high aesthetic appeal , 2013, Front. Hum. Neurosci..

[17]  Deborah J. Aks,et al.  Quantifying Aesthetic Preference for Chaotic Patterns , 1996 .

[18]  Branka Spehar,et al.  Perceptual and Physiological Responses to Jackson Pollock's Fractals , 2011, Front. Hum. Neurosci..

[19]  Margaret E. Sereno,et al.  Navigation performance in virtual environments varies with fractal dimension of landscape. , 2016, Journal of environmental psychology.

[20]  D. Saupe Algorithms for random fractals , 1988 .

[21]  R.P. Taylor,et al.  Reduction of Physiological Stress Using Fractal Art and Architecture , 2006, Leonardo.

[22]  Margaret E. Sereno,et al.  Percepts from noise patterns: The role of fractal dimension in object pareidolia , 2016 .

[23]  B. Mandelbrot Fractal Geometry of Nature , 1984 .

[24]  Holger Wiese,et al.  Fourier Power Spectrum Characteristics of Face Photographs: Attractiveness Perception Depends on Low-Level Image Properties , 2015, PloS one.

[25]  Margaret E. Sereno,et al.  Aesthetic Responses to Exact Fractals Driven by Physical Complexity , 2016, Front. Hum. Neurosci..

[26]  J R Mureika,et al.  Multifractal structure in nonrepresentational art. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Rémy Allard,et al.  Exploring the spatiotemporal properties of fractal rotation perception. , 2009, Journal of vision.

[28]  Branka Spehar,et al.  Taxonomy of Individual Variations in Aesthetic Responses to Fractal Patterns , 2016, Front. Hum. Neurosci..

[29]  Ben R. Newell,et al.  Universal aesthetic of fractals , 2003, Comput. Graph..

[30]  Ralph Roskies,et al.  Fourier Descriptors for Plane Closed Curves , 1972, IEEE Transactions on Computers.

[31]  Donald S. Fussell,et al.  Computer rendering of stochastic models , 1998 .

[32]  R. Voss Characterization and Measurement of Random Fractals , 1986 .

[33]  T. Laike,et al.  Human physiological benefits of viewing nature: EEG responses to exact and statistical fractal patterns. , 2015, Nonlinear dynamics, psychology, and life sciences.

[34]  Julien Clinton Sprott,et al.  Automatic generation of strange attractors , 1993, Comput. Graph..

[35]  Stephane J. M. Rainville,et al.  Spatial-scale contribution to the detection of mirror symmetry in fractal noise. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[36]  G. J. Burton,et al.  Color and spatial structure in natural scenes. , 1987, Applied optics.

[37]  Thomas R Clandinin,et al.  Motion-detecting circuits in flies: coming into view. , 2014, Annual review of neuroscience.

[38]  Branka Spehar,et al.  Fractals in art and nature: why do we like them? , 2013, Electronic Imaging.

[39]  Harriet A Allen,et al.  Visual mechanisms of motion analysis and motion perception. , 2004, Annual review of psychology.

[40]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[41]  Daniel L. Ruderman,et al.  Origins of scaling in natural images , 1996, Vision Research.

[42]  Benoit B. Mandelbrot,et al.  Is Nature Fractal? , 1998, Science.

[43]  Richard P. Taylor,et al.  Beauty and the beholder: the role of visual sensitivity in visual preference , 2015, Front. Hum. Neurosci..

[44]  Daniel A. Lidar,et al.  Is the Geometry of Nature Fractal? , 1998, Science.

[45]  D. Field,et al.  Human discrimination of fractal images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[46]  Joachim Denzler,et al.  Statistical image properties of print advertisements, visual artworks and images of architecture , 2013, Front. Psychol..

[47]  David J. Field,et al.  Finding a face on Mars: a study on the priors for illusory objects , 2016 .

[48]  I. Good,et al.  Fractals: Form, Chance and Dimension , 1978 .

[49]  J. H. van Hateren,et al.  Modelling the Power Spectra of Natural Images: Statistics and Information , 1996, Vision Research.

[50]  Richard Taylor,et al.  A Complex Story: Universal Preference vs. Individual Differences Shaping Aesthetic Response to Fractals Patterns , 2016, Front. Hum. Neurosci..

[51]  J. H. Hateren,et al.  Theoretical predictions of spatiotemporal receptive fields of fly LMCs, and experimental validation , 1992, Journal of Comparative Physiology A.