RPoulsonPsych221Project

Introduction

A beautifully rendered image on a computer screen or cell phone is the result of complex algorithms, careful measurements, intrinsically elegant machinery, and hard work. Designers must take into account the limitations and brilliance of the human visual system in order to produce an outcome that looks as close to the real scene as possible. Through a variety of processes, accounting for different technical limitations as well as human-related issues, a vivid replica is created for viewing delight. One of these steps is that of creating color constancy (or chromatic adaptation)-- or specifically, mimicking the human visual system’s ability to perceive the color of an object or a scene of objects as identical, not matter what the illumination on the object truly is (Gevers & Gijsenij, 2011). This feature of the human visual system is necessary to correctly identify features of objects. For example, an apple viewed under the fluorescent light of a kitchen is red, but the same apple is also red when viewed in daylight.

Specifically, my project dealt with altering the illumination of a painting, and attempting to create color constancy with a variety of methods to find the most closely depicted replica to a direct rending of the image under a preferred light source. For instance, my preferred light source was D65, or daylight, and I changed the illumination on the image to fluorescent and tried a variety of transforms to perform color balancing on the resulting image. These transformations occurred on a hyperspectral image of “Virgin, Child and St. John,” a painting by 15th century Italian artist Jacopo del Sellaio, which is currently on display at the Cantor Art Center.

Methods

Changing the illuminant of an image is simply – one needs only to apply a linear transform of the color matching transforms. The more computationally interesting component is creating color balancing. I set the illumination on the Sellaio Face image to one of five different lights (D50, D75, Fluorescent, Fluorescent11, and Tungsten); I then created four different transforms to attempt color constancy/balancing. The resulting image was analyzed using the Delta E value to find the best match.

Simple White Point XYZ Scaling

The creation of color constancy is possible through a variety of methods. In a simple first attempt, I created a very simple transform to create an easy diagonal transform. Taking a cue from the color-balancing lecture, I sampled the XYZ values of a white point in the image under the current illumination and the illumination into which I wished to convert. This is labeled the “White Conversion.” For thoroughness, I also created a full 3x3 matrix using a sample of white points to create a more rich transformation.

Full Image XYZ Scaling

In addition to the white conversion, I also used a script that created a transform by using the entirety of the images in relation to one another instead of simply using single points. These transforms were created both in the full 3x3 form and just the diagonal for comparisons. Specifically this script solved for the transformation of one XYZ into another, satisfying

\begin{bmatrix} 0 & \cdots & 0 \\ \vdots & \ddots & \vdots \\ 0 & \cdots & 0 \end{bmatrix}