Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Edge-Aware Image Color Appearance and Difference Modeling

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

The von Kries hypothesis and a basis for color constancy

Color constancy is almost exclusively modeled with diagonal transforms. However, the choice of basis under which diagonal transforms are taken is traditionally ad hoc. Attempts to remedy the situation have been hindered by the fact that no joint characterization of the conditions for {sensors, illuminants, reflectances} to support diagonal color constancy has previously been achieved. In this work, we observe that the von Kries compatibility conditions are impositions only on the sensor measurements, not the physical spectra. This allows us to formulate the von Kries compatibility conditions succinctly as rank constraints on an order 3 measurement tensor. Given this, we propose an algorithm that computes a (locally) optimal choice of color basis for diagonal color constancy and compare the results against other proposed choices.Engineering and Applied Science

Rendering HDR images

Color imaging systems are continuously improving, and have now improved to the point of capturing high dynamic range scenes. Unfortunately most commercially available color display devices, such as CRTs and LCDs, are limited in their dynamic range. It is necessary to tone-map, or render, the high dynamic range images in order to display them onto a lower dynamic range device. This paper describes the use of an image appearance model, iCAM, to render high dynamic range images for display. Image appearance models have greater flexibility over dedicated tone-scaling algorithms as they are designed to predict how images perceptually appear, and not designed for the singular purpose of rendering. In this paper we discuss the use of an image appearance framework, and describe specific implementation details for using that framework to render high dynamic range images

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

A Psychophysical Oriented Saliency Map Prediction Model

Visual attention is one of the most significant characteristics for selecting and understanding the outside redundancy world. The human vision system cannot process all information simultaneously, due to the visual information bottleneck. In order to reduce the redundant input of visual information, the human visual system mainly focuses on dominant parts of scenes. This is commonly known as visual saliency map prediction. This paper proposed a new psychophysical saliency prediction architecture, WECSF, inspired by multi-channel model of visual cortex functioning in humans. The model consists of opponent color channels, wavelet transform, wavelet energy map, and contrast sensitivity function for extracting low-level image features and providing maximum approximation to the human visual system. The proposed model is evaluated using several datasets, including the MIT1003, MIT300, TORONTO, SID4VAM, and UCF Sports datasets. We also quantitatively and qualitatively compare the saliency prediction performance with that of other state-of-the-art models. Our model achieved strongly stable and better performance with different metrics on nature images, psychophysical synthetic images and dynamic videos. Additionally, we found that Fourier and spectral-inspired saliency prediction models outperformed other state-of-the-art non-neural network and even deep neural network models on psychophysical synthetic images, it can be explained and supported the Fourier Vision Hypothesis. Finally, the proposed model could be used as a computational model of primate vision system and help us understand mechanism of vision system

Quantitative studies of animal colour constancy:using the chicken as a model

Colour constancy is the capacity of visual systems to keep colour perception constant despite changes in the illumination spectrum. Colour constancy has been tested extensively in humans and has also been described in many animals. In humans, colour constancy is often studied quantitatively, but besides humans, this has only been done for the goldfish and the honeybee. In this study, we quantified colour constancy in the chicken by training the birds in a colour discrimination task and testing them in changed illumination spectra to find the largest illumination change in which they were able to remain colour-constant. We used the receptor noise limited model for animal colour vision to quantify the illumination changes, and found that colour constancy performance depended on the difference between the colours used in the discrimination task, the training procedure and the time the chickens were allowed to adapt to a new illumination before making a choice. We analysed literature data on goldfish and honeybee colour constancy with the same method and found that chickens can compensate for larger illumination changes than both. We suggest that future studies on colour constancy in non-human animals could use a similar approach to allow for comparison between species and populations

Estimation of illuminants from color signals of illuminated objects

Color constancy is the ability of the human visual systems to discount the effect of the illumination and to assign approximate constant color descriptions to objects. This ability has long been studied and widely applied to many areas such as color reproduction and machine vision, especially with the development of digital color processing. This thesis work makes some improvements in illuminant estimation and computational color constancy based on the study and testing of existing algorithms. During recent years, it has been noticed that illuminant estimation based on gamut comparison is efficient and simple to implement. Although numerous investigations have been done in this field, there are still some deficiencies. A large part of this thesis has been work in the area of illuminant estimation through gamut comparison. Noting the importance of color lightness in gamut comparison, and also in order to simplify three-dimensional gamut calculation, a new illuminant estimation method is proposed through gamut comparison at separated lightness levels. Maximum color separation is a color constancy method which is based on the assumption that colors in a scene will obtain the largest gamut area under white illumination. The method was further derived and improved in this thesis to make it applicable and efficient. In addition, some intrinsic questions in gamut comparison methods, for example the relationship between the color space and the application of gamut or probability distribution, were investigated. Color constancy methods through spectral recovery have the limitation that there is no effective way to confine the range of object spectral reflectance. In this thesis, a new constraint on spectral reflectance based on the relative ratios of the parameters from principal component analysis (PCA) decomposition is proposed. The proposed constraint was applied to illuminant detection methods as a metric on the recovered spectral reflectance. Because of the importance of the sensor sensitivities and their wide variation, the influence from the sensor sensitivities on different kinds of illuminant estimation methods was also studied. Estimation method stability to wrong sensor information was tested, suggesting the possible solution to illuminant estimation on images with unknown sources. In addition, with the development of multi-channel imaging, some research on illuminant estimation for multi-channel images both on the correlated color temperature (CCT) estimation and the illuminant spectral recovery was performed in this thesis. All the improvement and new proposed methods in this thesis are tested and compared with those existing methods with best performance, both on synthetic data and real images. The comparison verified the high efficiency and implementation simplicity of the proposed methods