Edge-Aware Image Color Appearance and Difference Modeling
This work addresses the challenge of accurately modeling color perception in images for applications in computer vision and graphics, but it appears incremental as it builds on existing models.
The paper tackled the problem of adapting traditional color appearance and difference models to complex image stimuli by proposing edge-aware mechanisms, resulting in improved image difference predictions through the application of contrast sensitivity functions and local adaptation rules.
The perception of color is one of the most important aspects of human vision. From an evolutionary perspective, the accurate perception of color is crucial to distinguishing friend from foe, and food from fatal poison. As a result, humans have developed a keen sense of color and are able to detect subtle differences in appearance, while also robustly identifying colors across illumination and viewing conditions. In this paper, we shall briefly review methods for adapting traditional color appearance and difference models to complex image stimuli, and propose mechanisms to improve their performance. In particular, we find that applying contrast sensitivity functions and local adaptation rules in an edge-aware manner improves image difference predictions.