[IPOL announce] new article: The Portilla-Simoncelli Texture Model: towards Understanding the Early Visual Cortex

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2021/324/

Jonathan Vacher, and Thibaud Briand,
The Portilla-Simoncelli Texture Model: towards Understanding the Early 
Visual Cortex,
Image Processing On Line, 11 (2021), pp. 170–211.
https://doi.org/10.5201/ipol.2021.324

Abstract

Texture synthesis is a prolific subarea in computer vision where 
statistical methods are often successful. The Portilla and Simoncelli 
(PS) texture algorithm is one of such methods that became very popular 
and has influenced visual perception studies. For many reasons it can 
still be considered as a state-of-the art texture synthesis algorithm: 
(i) it generates textures that are often indistinguishable from the 
original without scrutiny; (ii) it relies on few parameters compared to 
recent deep learning methods; (iii) recent algorithms often compare to 
it. Here, we review the scientific impact of this algorithm and give a 
detailed explanation. Briefly, the PS algorithm synthesizes a new 
texture by iteratively imposing to a Gaussian white noise image a set of 
high-order statistics of wavelet coefficients precomputed on a texture 
example. After few iterations the initial white noise image is 
transformed into a texture that is similar to the texture example. We 
provide a fast C++ implementation, evaluate the effect of the algorithm 
parameters and illustrate its capabilities with many synthesis examples. 
In addition, we propose two notable new features to the original 
implementation: (i) the possibility to interpolate between two textures; 
(ii) the possibility to handle non-periodicity using the 
'periodic+smooth' decomposition.