Multidimensional image selection and classification system based on visual feature extraction and scaling

Sorting and searching operations used for the selection of test images strongly affect the results of image quality investigations and require a high level of versatility. This paper describes the way that inherent image properties, which are known to have a visual impact on the observer, can be used to provide support and an innovative answer to image selection and classification. The selected image properties are intended to be comprehensive and to correlate with our perception. Results from this work aim to lead to the definition of a set of universal scales of perceived image properties that are relevant to image quality assessments. The initial prototype built towards these objectives relies on global analysis of low-level image features. A multidimensional system is built, based upon the global image features of: lightness, contrast, colorfulness, color contrast, dominant hue(s) and busyness. The resulting feature metric values are compared against outcomes from relevant psychophysical investigations to evaluate the success of the employed algorithms in deriving image features that affect the perceived impression of the images

High-fidelity colour reproduction for high-dynamic-range imaging

The aim of this thesis is to develop a colour reproduction system for high-dynamic-range (HDR) imaging. Classical colour reproduction systems fail to reproduce HDR images because current characterisation methods and colour appearance models fail to cover the dynamic range of luminance present in HDR images. HDR tone-mapping algorithms have been developed to reproduce HDR images on low-dynamic-range media such as LCD displays. However, most of these models have only considered luminance compression from a photographic point of view and have not explicitly taken into account colour appearance. Motivated by the idea to bridge the gap between crossmedia colour reproduction and HDR imaging, this thesis investigates the fundamentals and the infrastructure of cross-media colour reproduction. It restructures cross-media colour reproduction with respect to HDR imaging, and develops a novel cross-media colour reproduction system for HDR imaging. First, our HDR characterisation method enables us to measure HDR radiance values to a high accuracy that rivals spectroradiometers. Second, our colour appearance model enables us to predict human colour perception under high luminance levels. We first built a high-luminance display in order to establish a controllable high-luminance viewing environment. We conducted a psychophysical experiment on this display device to measure perceptual colour attributes. A novel numerical model for colour appearance was derived from our experimental data, which covers the full working range of the human visual system. Our appearance model predicts colour and luminance attributes under high luminance levels. In particular, our model predicts perceived lightness and colourfulness to a significantly higher accuracy than other appearance models. Finally, a complete colour reproduction pipeline is proposed using our novel HDR characterisation and colour appearance models. Results indicate that our reproduction system outperforms other reproduction methods with statistical significance. Our colour reproduction system provides high-fidelity colour reproduction for HDR imaging, and successfully bridges the gap between cross-media colour reproduction and HDR imaging

A Paradigm for color gamut mapping of pictorial images

In this thesis, a paradigm was generated for color gamut mapping of pictorial images. This involved the development and testing of: 1.) a hue-corrected version of the CIELAB color space, 2.) an image-dependent sigmoidal-lightness-rescaling process, 3.) an image-gamut- based chromatic-compression process, and 4.) a gamut-expansion process. This gamut-mapping paradigm was tested against some gamut-mapping strategies published in the literature. Reproductions generated by gamut mapping in a hue-corrected CIELAB color space more accurately preserved the perceived hue of the original scenes compared to reproductions generated using the CIELAB color space. The results of three gamut-mapping experiments showed that the contrast-preserving nature of the sigmoidal-lightness-remapping strategy generated gamut-mapped reproductions that were better matches to the originals than reproductions generated using linear-lightness-compression functions. In addition, chromatic-scaling functions that compressed colors at a higher rate near the gamut surface and less near the achromatic axis produced better matches to the originals than algorithms that performed linear chroma compression throughout color space. A constrained gamut-expansion process, similar to the inverse of the best gamut-compression process found in this experiment, produced reproductions preferred over an expansion process utilizing unconstrained linear expansion

Evaluation of color differences in natural scene color images

Since there is a wide range of applications requiring image color difference (CD) assessment (e.g. color quantization, color mapping), a number of CD measures for images have been proposed. However, the performance evaluation of such measures often suffers from the following major flaws: (1) test images contain primarily spatial- (e.g. blur) rather than color-specific distortions (e.g. quantization noise), (2) there are too few test images (lack of variability in color content), and (3) test images are not publicly available (difficult to reproduce and compare). Accordingly, the performance of CD measures reported in the state-of-the-art is ambiguous and therefore inconclusive to be used for any specific color-related application. In this work, we review a total of twenty four state-of-the-art CD measures. Then, based on the findings of our review, we propose a novel method to compute CDs in natural scene color images. We have tested our measure as well as the state-of-the-art measures on three color related distortions from a publicly available database (mean shift, change in color saturation and quantization noise). Our experimental results show that the correlation between the subjective scores and the proposed measure exceeds 85% which is better than the other twenty four CD measures tested in this work (for illustration the best performing state-of-the-art CD measures achieve correlations with humans lower than 80%)

Measuring Perceptual Color Differences of Smartphone Photographs

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of smartphone photography characterized by greater content complexities and learning-based image signal processors. In this paper, we put together so far the largest image dataset for perceptual CD assessment, in which the photographic images are 1) captured by six flagship smartphones, 2) altered by Photoshop, 3) post-processed by built-in filters of the smartphones, and 4) reproduced with incorrect color profiles. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30,000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data, and empirically behaves as a proper metric in the mathematical sense. Our dataset and code are publicly available at https://github.com/hellooks/CDNet.Comment: 10 figures, 8 tables, 14 page

Crowd-sourced data and its applications for new algorithms in photographic imaging

This thesis comprises two main themes. The first of these is concerned primarily with the validity and utility of data acquired from web-based psychophysical experiments. In recent years web-based experiments, and the crowd-sourced data they can deliver, have been rising in popularity among the research community for several key reasons – primarily ease of administration and easy access to a large population of diverse participants. However, the level of control with which traditional experiments are performed, and the severe lack of control we have over web-based alternatives may lead us to believe that these benefits come at the cost of reliable data. Indeed, the results reported early in this thesis support this assumption. However, we proceed to show that it is entirely possible to crowd-source data that is comparable with lab-based results. The second theme of the thesis explores the possibilities presented by the use of crowd-sourced data, taking a popular colour naming experiment as an example. After using the crowd-sourced data to construct a model for computational colour naming, we consider the value of colour names as image descriptors, with particular relevance to illuminant estimation and object indexing. We discover that colour names represent a particularly useful quantisation of colour space, allowing us to construct compact image descriptors for object indexing. We show that these descriptors are somewhat tolerant to errors in illuminant estimation and that their perceptual relevance offers even further utility. We go on to develop a novel algorithm which delivers perceptually-relevant, illumination-invariant image descriptors based on colour names

N-colour separation methods for accurate reproduction of spot colours

In packaging, spot colours are used to print key information like brand logos and elements for which the colour accuracy is critical. The present study investigates methods to aid the accurate reproduction of these spot colours with the n-colour printing process. Typical n-colour printing systems consist of supplementary inks in addition to the usual CMYK inks. Adding these inks to the traditional CMYK set increases the attainable colour gamut, but the added complexity creates several challenges in generating suitable colour separations for rendering colour images. In this project, the n-colour separation is achieved by the use of additional sectors for intermediate inks. Each sector contains four inks with the achromatic ink (black) common to all sectors. This allows the extension of the principles of the CMYK printing process to these additional sectors. The methods developed in this study can be generalised to any number of inks. The project explores various aspects of the n-colour printing process including the forward characterisation methods, gamut prediction of the n-colour process and the inverse characterisation to calculate the n-colour separation for target spot colours. The scope of the study covers different printing technologies including lithographic offset, flexographic, thermal sublimation and inkjet printing. A new method is proposed to characterise the printing devices. This method, the spot colour overprint (SCOP) model, was evaluated for the n-colour printing process with different printing technologies. In addition, a set of real-world spot colours were converted to n-colour separations and printed with the 7-colour printing process to evaluate against the original spot colours. The results show that the proposed methods can be effectively used to replace the spot coloured inks with the n-colour printing process. This can save significant material, time and costs in the packaging industry

Accurate Colour Reproduction of Human Face using 3D Printing Technology

The colour of the face is one of the most significant factors in appearance and perception of an individual. With the rapid development of colour 3D printing technology and 3D imaging acquisition techniques, it is possible to achieve skin colour reproduction with the application of colour management. However, due to the complicated skin structure with uneven and non-uniform surface, it is challenging to obtain accurate skin colour appearance and reproduce it faithfully using 3D colour printers. The aim of this study was to improve the colour reproduction accuracy of the human face using 3D printing technology. A workflow of 3D colour image reproduction was developed, including 3D colour image acquisition, 3D model manipulation, colour management, colour 3D printing, postprocessing and colour reproduction evaluation. Most importantly, the colour characterisation methods for the 3D imaging system and the colour 3D printer were comprehensively investigated for achieving higher accuracy