A computational framework for colour metrics and colour space transforms

Ivar Farup

doi:10.7717/peerj-cs.48

Abstract

An object-oriented computational framework for the transformation of colour data and colour metric tensors is presented. The main idea of the design is to represent the transforms between spaces as compositions of objects from a class hierarchy providing the methods for both the transforms themselves and the corresponding Jacobian matrices. In this way, new colour spaces can be implemented on the fly by transforming from any existing colour space, and colour data in various formats as well as colour metric tensors and colour difference data can easily be transformed between the colour spaces. This reduces what normally requires several days of coding to a few lines of code without introducing a significant computational overhead. The framework is implemented in the Python programming language.

Highlights

Colour data such as measured colours, specified colours or pixels of colour images are most commonly described as sets of points in a three-dimensional space—a so-called colour space
The complexity of conversion is present for computations involving colour metric data, which, by nature, is tensorial (Deza & Deza, 2009), giving rise to the need for the direct transformations, and the corresponding Jacobian matrices—a tedious and error-prone process (Pant & Farup, 2012)
Transformations between different colour spaces can in general take the shape of a function, x = x(x), where x = (x1,x2,x3)T represents a colour, i.e., a point in a colour space

Summary

INTRODUCTION

Colour data such as measured colours, specified colours or pixels of colour images are most commonly described as sets of points in a three-dimensional space—a so-called colour space. Many different colour spaces are currently in use in various settings. The complexity of conversion is present for computations involving colour metric data, which, by nature, is tensorial (Deza & Deza, 2009), giving rise to the need for the direct transformations, and the corresponding Jacobian matrices—a tedious and error-prone process (Pant & Farup, 2012). No common framework for such transformations of colour data and metrics including the automated computation of Jacobian matrices has been constructed. To demonstrate to which degree the framework simplifies the implementation of colour data and metric transformations, an implementation of the framework using the high-level programming language Python (Van Rossum & Drake, 1995) is applied to some standard example problems

BACKGROUND

CONCLUSION