Towards Colour Imaging with the Image Ranger

Many of the colour images captured by different types of digital camera do not provide quality colour image according to human visual perception. In this study we explore technique for colour correction of the colour images from the Waikato Image Ranger. Colour images were captured using three different illuminants with the Waikato Image Ranger. The colour image formed from the ranger data do not have good quality because illuminants used do not match usual RGB standard illuminants. The spectral power distribution values of the illuminants were measured using spectroradiometer. To calculate tristimulus values the reflectance function of the scene is required. A mechanism of calculating the reflectance functions from the ranger data using genetic algorithm was explained. The reflectance functions are approximated using variable Gaussian basis functions, and fit to the ranger colour triplets by genetic algorithms. From the estimated reflectance functions standard CRT RGB values were calculated. It was found that the genetic algorithm approach was for too slow for practical purposes and produced images with far too much colour variation

The LLAB model for quantifying colour appearance

A reliable colour appearance model is desired by industry to achieve high colour fidelity between images produced using a range of different imaging devices. The aim of this study was to derive a reliable colour appearance model capable of predicting the change of perceived attributes of colour appearance under a wide range of media/viewing conditions. The research was divided into three parts: characterising imaging devices, conducting a psychophysical experiment, and developing a colour appearance model. Various imaging devices were characterised including a graphic art scanner, a Cromalin proofing system, an IRIS ink jet printer, and a Barco Calibrator. For the former three devices, each colour is described by four primaries: cyan (C), magenta (M), yellow (Y), and black (K). Three set of characterisation samples (120 and 31 black printer, and cube data sets) were produced and measured for deriving and testing the printing characterisation models. Four black printer algorithms (BPA), were derived. Each included both forward and reverse processes. A 2nd BPA printing model taking into account additivity failure, grey component replacement (GCR) algorithm gave the most accurate prediction to the characterisation data set than the other BPA models. The PLCC (Piecewise Linear interpolation assuming Constant Chromaticity coordinates) monitor model was also implemented to characterise the Barco monitor. The psychophysical experiment was conducted to compare Cromalin hardcopy images viewed in a viewing cabinet and softcopy images presented on a monitor under a wide range of illuminants (white points) including: D93, D65, D50 and A. Two scaling methods: category judgement and paired comparison, were employed by viewing a pair of images. Three classes of colour models were evaluated: uniform colour spaces, colour appearance models and chromatic adaptation transforms. Six images were selected and processed via each colour model. The results indicated that the BFD chromatic transform gave the most accurate predictions of the visual results. Finally, a colour appearance model, LLAB, was developed. It is a combination of the BFD chromatic transform and a modified version of CIELAB uniform colour space to fit the LUTCRI Colour Appearance Data previously accumulated. The form of the LLAB model is much simpler and its performance is more precise to fit experimental data than those of the other models

The science of color and color vision

A survey of color science and color vision