The receptor noise limited (RNL) chromaticity colour space is convenient because the Euclidean distance between any two points in this space is equal to the Delta-S of the RNL model (in units of “just noticeable differences”, JNDs). This means that
Colour Maps
Colour measurements have historically been plotted and compared as single points in a colour space (such as a Maxwell triangle, or a tetrahedral colour space). The area or volumes occupied by these average colour points has also been used to
Boundary Strength Analysis
The Boundary Strength Analysis (BSA) is essentially a combination and extention of both the Adjacency Analyis and the Visual Contrast Analysis, published by John Endler et al. in 2018 using Matlab. It uses the off-diagonal of the transition matrix to
Visual Contrast Analysis
The’Visual Contrast Analysis’ is an umbrella term for a variety of pattern parameters which were first conceptualised by John Endler in the early 90s (Endler 1990, 1991, 1992; Endler & Mielke 2005). These parameters seek to combine both chromatic and
Adjacency Analysis
The Adjacency Analysis (Endler 2012) is an analytical framework designed to capture colour pattern geometry. It is based on the concept of running horizontal and vertical sampling transects over a segmented image (Fig. 1) where the transitions from one pixel
AcuityView 2.0
Acuity correction is an important step in modelling receiver vision, and in the QCPA framework, controlling for the receiver’s spatial acuity, and the viewing distance. Initially developed in Matlab by Sönke Johnsen (Based on well-known Fourier math), later adapted for
Local Edge Intensity Analysis (LEIA)
The Local Edge Intensity Analysis (LEIA) measures edge intensities across a one-pixel-neighbourhood in four directions: horizontal, vertical and both diagonals. The contrast is measured as the Euclidean distance in the log-transformed Receptor Noise Limited (RNL) colour space and provided as
RNL Ranked Filter
The Receptor Noise Limited (RNL) Ranked Filter performs noise reduction while preserving chromatic or luminance edges, and is an important step in the QCPA framework. Importantly, it is also able to reconstruct sharp edges in images which have undergone smoothing
RNL Clustering
The RNL clustering technique was developed specifically for the QCPA workflow, parts of which require a colour image be segmented into different pattern elements or ‘clusters’. Unlike previous approaches this technique uses log receptor noise limited (RNL) modelling of Delta-S