Preserving Perceptual Contrast in Decolorization with Optimized Color Orders

Converting a color image to a grayscale image, namely decolorization, is an important process for many real-world applications. Previous methods build contrast loss functions to minimize the contrast differences between the color images and the resultant grayscale images. In this paper, we improve upon a widely used decolorization method with two extensions. First, we relax the need for heuristics on color orders, which the baseline method relies on when computing the contrast differences. In our method, the color orders are incorporated into the loss function and are determined through optimization. Moreover, we apply a nonlinear function on the grayscale contrast to better model human perception of contrast. Both qualitative and quantitative results on the standard benchmark demonstrate the effectiveness of our two extensions

Spectral methods for multimodal data analysis

Spectral methods have proven themselves as an important and versatile tool in a wide range of problems in the fields of computer graphics, machine learning, pattern recognition, and computer vision, where many important problems boil down to constructing a Laplacian operator and finding a few of its eigenvalues and eigenfunctions. Classical examples include the computation of diffusion distances on manifolds in computer graphics, Laplacian eigenmaps, and spectral clustering in machine learning. In many cases, one has to deal with multiple data spaces simultaneously. For example, clustering multimedia data in machine learning applications involves various modalities or ``views'' (e.g., text and images), and finding correspondence between shapes in computer graphics problems is an operation performed between two or more modalities. In this thesis, we develop a generalization of spectral methods to deal with multiple data spaces and apply them to problems from the domains of computer graphics, machine learning, and image processing. Our main construction is based on simultaneous diagonalization of Laplacian operators. We present an efficient numerical technique for computing joint approximate eigenvectors of two or more Laplacians in challenging noisy scenarios, which also appears to be the first general non-smooth manifold optimization method. Finally, we use the relation between joint approximate diagonalizability and approximate commutativity of operators to define a structural similarity measure for images. We use this measure to perform structure-preserving color manipulations of a given image

Colour and Colorimetry Multidisciplinary Contributions Vol. XIb

It is well known that the subject of colour has an impact on a range of disciplines. Colour has been studied in depth for many centuries, and as well as contributing to theoretical and scientific knowledge, there have been significant developments in applied colour research, which has many implications for the wider socio-economic community. At the 7th Convention of Colorimetry in Parma, on the 1st October 2004, as an evolution of the previous SIOF Group of Colorimetry and Reflectoscopy founded in 1995, the "Gruppo del Colore" was established. The objective was to encourage multi and interdisciplinary collaboration and networking between people in Italy that addresses problems and issues on colour and illumination from a professional, cultural and scientific point of view. On the 16th of September 2011 in Rome, in occasion of the VII Color Conference, the members assembly decided to vote for the autonomy of the group. The autonomy of the Association has been achieved in early 2012. These are the proceedings of the English sessions of the XI Conferenza del Colore

Efficient and effective objective image quality assessment metrics

Acquisition, transmission, and storage of images and videos have been largely increased in recent years. At the same time, there has been an increasing demand for high quality images and videos to provide satisfactory quality-of-experience for viewers. In this respect, high dynamic range (HDR) imaging with higher than 8-bit depth has been an interesting approach in order to capture more realistic images and videos. Objective image and video quality assessment plays a significant role in monitoring and enhancing the image and video quality in several applications such as image acquisition, image compression, multimedia streaming, image restoration, image enhancement and displaying. The main contributions of this work are to propose efficient features and similarity maps that can be used to design perceptually consistent image quality assessment tools. In this thesis, perceptually consistent full-reference image quality assessment (FR-IQA) metrics are proposed to assess the quality of natural, synthetic, photo-retouched and tone-mapped images. In addition, efficient no-reference image quality metrics are proposed to assess JPEG compressed and contrast distorted images. Finally, we propose a perceptually consistent color to gray conversion method, perform a subjective rating and evaluate existing color to gray assessment metrics. Existing FR-IQA metrics may have the following limitations. First, their performance is not consistent for different distortions and datasets. Second, better performing metrics usually have high complexity. We propose in this thesis an efficient and reliable full-reference image quality evaluator based on new gradient and color similarities. We derive a general deviation pooling formulation and use it to compute a final quality score from the similarity maps. Extensive experimental results verify high accuracy and consistent performance of the proposed metric on natural, synthetic and photo retouched datasets as well as its low complexity. In order to visualize HDR images on standard low dynamic range (LDR) displays, tone-mapping operators are used in order to convert HDR into LDR. Given different depth bits of HDR and LDR, traditional FR-IQA metrics are not able to assess the quality of tone-mapped images. The existing full-reference metric for tone-mapped images called TMQI converts both HDR and LDR to an intermediate color space and measure their similarity in the spatial domain. We propose in this thesis a feature similarity full-reference metric in which local phase of HDR is compared with the local phase of LDR. Phase is an important information of images and previous studies have shown that human visual system responds strongly to points in an image where the phase information is ordered. Experimental results on two available datasets show the very promising performance of the proposed metric. No-reference image quality assessment (NR-IQA) metrics are of high interest because in the most present and emerging practical real-world applications, the reference signals are not available. In this thesis, we propose two perceptually consistent distortion-specific NR-IQA metrics for JPEG compressed and contrast distorted images. Based on edge statistics of JPEG compressed images, an efficient NR-IQA metric for blockiness artifact is proposed which is robust to block size and misalignment. Then, we consider the quality assessment of contrast distorted images which is a common distortion. Higher orders of Minkowski distance and power transformation are used to train a low complexity model that is able to assess contrast distortion with high accuracy. For the first time, the proposed model is used to classify the type of contrast distortions which is very useful additional information for image contrast enhancement. Unlike its traditional use in the assessment of distortions, objective IQA can be used in other applications. Examples are the quality assessment of image fusion, color to gray image conversion, inpainting, background subtraction, etc. In the last part of this thesis, a real-time and perceptually consistent color to gray image conversion methodology is proposed. The proposed correlation-based method and state-of-the-art methods are compared by subjective and objective evaluation. Then, a conclusion is made on the choice of the objective quality assessment metric for the color to gray image conversion. The conducted subjective ratings can be used in the development process of quality assessment metrics for the color to gray image conversion and to test their performance

Impact of image quality on SfM Photogrammetry : colour, compression and noise

Structure-from-motion (SfM) photogrammetry has become ubiquitous in the geosciences, owing to its low-cost and ease of use for generating 3D data. Ideas around data collection, quality and processing need to be revisited to ensure that the technology is being harnessed correctly. One area which is new in this current image acquisition boom is the range of sensors and systems being used to collect image data. This raises crucial questions in the geoscience community which are addressed in this contribution. This is split into three parts. Firstly, image quality is investigated to establish whether a stable association between it and the quality of photogrammetric products can be uncovered to allow simpler and more effective inter-comparison of results between studies. This was accomplished by artificially degrading a very high-quality benchmark dataset of a coastal cliff and a landslide in Norfolk, UK. Results revealed that the level of noise, image compression and downsampling all degrade the quality of 3D products from the SfM workflow. Secondly, these sets of images were pre-processed to establish whether results could be augmented by controlling the single colour channel used during photogrammetric processing. Results showed slight variations in the products generated, with evidence supporting the fine sensitivity SfM has for refining the focal length estimation of the lens. For extremely specific contexts, pre-processing of the RGB-to-single channel conversion may be relevant, but for the datasets analysed in this contribution this was not the case. Thirdly, image network configurations were investigated to build on previous research in establishing best practice. Results show that, in situations where the number of images being acquired is a limiting factor, networks with narrowly oblique overlapping images have a higher density and lower error than those with widely oblique images and those directly facing the surface normal. These results demonstrate the value of optimising image acquisition, and in the handling of this imagery. The differences in image quality and pre-processing which are unreported within geoscientific studies using SfM could account for differences between accuracies obtained, independent of the specific photogrammetric methods used. Insights from this work into how best to capture, process and produce the best quality SfM data will allow the community to adopt these best practices in the future