Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

What is the best luminance contrast weighting-function for image quality optimization? Traditionally measured contrast sensitivity functions (CSFs), have been often used as weighting-functions in image quality and difference metrics. Such weightings have been shown to result in increased sharpness and perceived quality of test images. We suggest contextual CSFs (cCSFs) and contextual discrimination functions (cVPFs) should provide bases for further improvement, since these are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context of complex masking information. Image quality assessment is understood to require detection and discrimination of masked signals, making contextual sensitivity and discrimination functions directly relevant. In this investigation, test images are weighted with a traditional CSF, cCSF, cVPF and a constant function. Controlled mutations of these functions are also applied as weighting-functions, seeking the optimal spatial frequency band weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice psychophysical tests. We show that maximal quality for our test images, results from cCSFs and cVPFs, mutated to boost contrast in the higher visible frequencies

Scene-Dependency of Spatial Image Quality Metrics

This thesis is concerned with the measurement of spatial imaging performance and the modelling of spatial image quality in digital capturing systems. Spatial imaging performance and image quality relate to the objective and subjective reproduction of luminance contrast signals by the system, respectively; they are critical to overall perceived image quality. The Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS) describe the signal (contrast) transfer and noise characteristics of a system, respectively, with respect to spatial frequency. They are both, strictly speaking, only applicable to linear systems since they are founded upon linear system theory. Many contemporary capture systems use adaptive image signal processing, such as denoising and sharpening, to optimise output image quality. These non-linear processes change their behaviour according to characteristics of the input signal (i.e. the scene being captured). This behaviour renders system performance “scene-dependent” and difficult to measure accurately. The MTF and NPS are traditionally measured from test charts containing suitable predefined signals (e.g. edges, sinusoidal exposures, noise or uniform luminance patches). These signals trigger adaptive processes at uncharacteristic levels since they are unrepresentative of natural scene content. Thus, for systems using adaptive processes, the resultant MTFs and NPSs are not representative of performance “in the field” (i.e. capturing real scenes). Spatial image quality metrics for capturing systems aim to predict the relationship between MTF and NPS measurements and subjective ratings of image quality. They cascade both measures with contrast sensitivity functions that describe human visual sensitivity with respect to spatial frequency. The most recent metrics designed for adaptive systems use MTFs measured using the dead leaves test chart that is more representative of natural scene content than the abovementioned test charts. This marks a step toward modelling image quality with respect to real scene signals. This thesis presents novel scene-and-process-dependent MTFs (SPD-MTF) and NPSs (SPDNPS). They are measured from imaged pictorial scene (or dead leaves target) signals to account for system scene-dependency. Further, a number of spatial image quality metrics are revised to account for capture system and visual scene-dependency. Their MTF and NPS parameters were substituted for SPD-MTFs and SPD-NPSs. Likewise, their standard visual functions were substituted for contextual detection (cCSF) or discrimination (cVPF) functions. In addition, two novel spatial image quality metrics are presented (the log Noise Equivalent Quanta (NEQ) and Visual log NEQ) that implement SPD-MTFs and SPD-NPSs. The metrics, SPD-MTFs and SPD-NPSs were validated by analysing measurements from simulated image capture pipelines that applied either linear or adaptive image signal processing. The SPD-NPS measures displayed little evidence of measurement error, and the metrics performed most accurately when they used SPD-NPSs measured from images of scenes. The benefit of deriving SPD-MTFs from images of scenes was traded-off, however, against measurement bias. Most metrics performed most accurately with SPD-MTFs derived from dead leaves signals. Implementing the cCSF or cVPF did not increase metric accuracy. The log NEQ and Visual log NEQ metrics proposed in this thesis were highly competitive, outperforming metrics of the same genre. They were also more consistent than the IEEE P1858 Camera Phone Image Quality (CPIQ) metric when their input parameters were modified. The advantages and limitations of all performance measures and metrics were discussed, as well as their practical implementation and relevant applications

Cardiac magnetic resonance imaging using an open 1.0T MR platform : a comparative study with a 1.5T tunnel system

Background: Cardiac magnetic resonance imaging (cMRI) has become the non-invasive reference standard for the evaluation of cardiac function and viability. The introduction of open, high-field, 1.0T (HFO) MR scanners offers advantages for examinations of obese, claustrophobic and paediatric patients. The aim of our study was to compare standard cMRI sequences from an HFO scanner and those from a cylindrical, 1.5T MR system. Material/Method: Fifteen volunteers underwent cMRI both in an open HFO and in a cylindrical MR system. The protocol consisted of cine and unenhanced tissue sequences. The signal-to-noise ratio (SNR) for each sequence and blood-myocardium contrast for the cine sequences were assessed. Image quality and artefacts were rated. The location and number of non-diagnostic segments was determined. Volunteers' tolerance to examinations in both scanners was investigated. Results: SNR was significantly lower in the HFO scanner (all p0.05). Overall, only few non-diagnostic myocardial segments were recorded: 6/960 (0.6%) by the HFO and 17/960 (1.8%) segments by the cylindrical system. The volunteers expressed a preference for the open MR system (p<0.01). Conclusions: Standard cardiac MRI sequences in an HFO platform offer a high image quality that is comparable to the quality of images acquired in a cylindrical 1.5T MR scanner. An open scanner design may potentially improve tolerance of cardiac MRI and therefore allow to examine an even broader patient spectrum

Visibility metrics and their applications in visually lossless image compression

Visibility metrics are image metrics that predict the probability that a human observer can detect differences between a pair of images. These metrics can provide localized information in the form of visibility maps, in which each value represents a probability of detection. An important application of the visibility metric is visually lossless image compression that aims at compressing a given image to the lowest fraction of bit per pixel while keeping the compression artifacts invisible at the same time. In previous works, most visibility metrics were modeled based on largely simplified assumptions and mathematical models of human visual systems. This approach generally fits well into experimental data measured with simple stimuli, such as Gabor patches. However, it cannot predict complex non-linear effects, such as contrast masking in natural images, particularly well. To predict visibility of image differences accurately, we collected the largest visibility dataset under fixed viewing conditions for calibrating existing visibility metrics and proposed a deep neural network-based visibility metric. We demonstrated in our experiments that the deep neural network-based visibility metric significantly outperformed existing visibility metrics. However, the deep neural network-based visibility metric cannot predict visibility under varying viewing conditions, such as display brightness and viewing distances that have great impacts on the visibility of distortions. To extend the deep neural network-based visibility metric to varying viewing conditions, we collected the largest visibility dataset under varying display brightness and viewing distances. We proposed incorporating white-box modules, in other words, luminance masking and viewing distance adaptation, into the black-box deep neural network, and we found that the combination of white-box modules and black-box deep neural networks could generalize our proposed visibility metric to varying viewing conditions. To demonstrate the application of our proposed deep neural network-based visibility metric to visually lossless image compression, we collected the visually lossless image compression dataset under fixed viewing conditions and significantly improved the deep neural network-based visibility metric's accuracy of predicting visually lossless image compression threshold by pre-training the visibility metric with a synthetic dataset generated by the state-of-the-art white-box visibility metric---HDR-VDP \cite{Mantiuk2011}. In a large-scale study of 1000 images, we found that with our improved visibility metric, we can save around 60\% to 70\% bits for visually lossless image compression encoding as compared to the default visually lossless quality level of 90. Because predicting image visibility and predicting image quality are closely related research topics, we also proposed a trained perceptually uniform transform for high dynamic range images and videos quality assessments by training a perceptual encoding function on a set of subjective quality assessment datasets. We have shown that when combining the trained perceptual encoding function with standard dynamic range image quality metrics, such as peak-signal-noise-ratio (PSNR), better performance was achieved compared to the untrained version

Quantification of impacts of colour on affective quality of images

Most consumer images serve emotional functions as well as informational ones. The impression of an image can be affected not only by physical properties e. g. size, colour and media but also by the context of images, aesthetic properties and social/personal backgrounds of observers. However in traditional frameworks in image quality studies, the impacts of colour-appearance attributes on image quality have focused on maximising the informational functions of images, considering an image as a reproduced copy of a real scene. Thus, a new approach was adopted in this study in an attempt to investigate the emotional aspect of an image. The goal of this research is to study the impact of colour-appearance attributes of an image on emotional responses, and to develop quantitative models for predicting emotional response considering the context of the image. To achieve this goal, three sets of psychophysical and physiological experiments have been conducted. First, the relationship between colour-appearance attributes and overall affective response to images was investigated for four different types of image contents. It was found that image colourfulness and lightness contrast had a consistent influence on these relationships for all types of images. The relationships between emotional responses of image pleasantness and excitement were significantly different between positive images and negative images. Accordingly, quantitative models of image pleasantness and excitement were developed as a function of image colourfulness and contrast separately for the two groups of images. Finally, models of image pleasantness and excitement for positive and negative images were developed as a linear equation based on models developed for each colour attribute. The relationships between colour-appearance attributes and responses on colour-emotion scales, active-passive, heavy-light and warm-cool, were also studied for four different types of image content. Quantitative models of the three colour-emotion scales were developed as a function of colour attributes of images such as lightness, colourfulness and lightness contrast. As an application of using the colour-emotion model developed for images, the relationships between colouremotion scales and image emotion were investigated and quantitative models of image pleasantness and excitement were developed as functions of three colouremotion scales for two groups of images: positive and negative. The model performance based on the colour-emotion scales was compared with the performance of models based on the colour attributes. As a result, the latter model performed better than former. The impact of image content and colour attributes of an image on emotional responses to images was investigated by measuring physiological responses to images which were compared with the psychophysical responses. It was found that the activities in skin conductance and heart rate showed significantly greater responses for the images with personal meanings and significances. For the effect of colour attributes in images, it was found that more chromatic images generated higher activity in skin conductance responses. It was also found that lower contrast images generated higher activity in corrugator EMG responsesEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Colour for behavioural success

Colour information not only helps sustain the survival of animal species by guiding sexual selection and foraging behaviour but also is an important factor in the cultural and technological development of our own species. This is illustrated by examples from the visual arts and from state-of-the-art imaging technology, where the strategic use of colour has become a powerful tool for guiding the planning and execution of interventional procedures. The functional role of colour information in terms of its potential benefits to behavioural success across the species is addressed in the introduction here to clarify why colour perception may have evolved to generate behavioural success. It is argued that evolutionary and environmental pressures influence not only colour trait production in the different species but also their ability to process and exploit colour information for goal-specific purposes. We then leap straight to the human primate with insight from current research on the facilitating role of colour cues on performance training with precision technology for image-guided surgical planning and intervention. It is shown that local colour cues in two-dimensional images generated by a surgical fisheye camera help individuals become more precise rapidly across a limited number of trial sets in simulator training for specific manual gestures with a tool. This facilitating effect of a local colour cue on performance evolution in a video-controlled simulator (pick-and-place) task can be explained in terms of colour-based figure-ground segregation facilitating attention to local image parts when more than two layers of subjective surface depth are present, as in all natural and surgical images