Gamut Compression and Extension Algorithms Based on Observer Experimental Data

Abstract

Gamut compression algorithms have traditionally been defined functionally and then tested with deductive methods, e.g., psychophysical experiments. Our study offers an alternative, an inductive method, in which observers judge image colors to represent the original images more accurately. We developed a computer‐controlled interactive tool that modifies the color appearance of pictorial images displayed on a monitor. In experiments, observers used the tool to alter color pixels according to the region of color space to which they belonged. We created three different gamut compression algorithms based on the observer experimental data. Observer groups evaluated the performance of the newly‐developed algorithms, existing gamut compression algorithms, and an image based on the average observers’ results from experiments in this study. The study of gamut extension is unlike the study of gamut compression in that it mainly deals with the degree of image pleasantness as judged by observers. The results of the gamut extension experiments in this study not only make available worthwhile data but also suggest a methodology for using the observer experimental tool for future gamut extension research.

Perceptually optimal boundaries for wide gamut TVs

“…The question then is how to map colors from a smaller gamut into a larger one or in other words how to use the extra space made available at the display. Up to now manufacturers have relied on color mapping/enhancement algorithms to handle this issue and as such there has been some research carried out that dealt with new mapping methodologies for mapping between small and large color gamuts 9,[12][13][14] .…”

Spectrum‐Based Color Reproduction Algorithm for Makeup Simulation of 3D Facial Avatar

“…The former method maps all colors outside the gamut onto the surface of the target device gamut. In contrast, the latter method compresses all the color in the reference device gamut into the target device gamut [15]. Thus, if the gamut difference is minimal, the clipping-based method is appropriate and vice versa.…”

Conjoint Analysis for Evaluating Parameterized Gamut Mapping Algorithms

“…We should point out that we are not the first who systematically include observer experiments in the development of GMAs; see, for example, the work done by Kang et al [14]. Multivariate analysis techniques also have been used in image processing to gauge the importance of parameters, see for example the book of Keelan [15], where the scaling between different parameters is ensured by using of "Just Noticeable Differences" (JNDs).…”