Recent years have witnessed the widespread application of High Dynamic Range (HDR) imaging, which like the Human Visual System (HVS), has the ability to capture a wide range of luminance values. Areas of application include home-entertainment, security, scientific imaging, video processing, computer graphics, multimedia communications, and healthcare. However, in practice, HDR content cannot be displayed in full on standard or low dynamic range (LDR) displays, and this diminishes the benefits of HDR technology for many users. To address this problem, Tone-Mapping Operators (TMO) are used to convert HDR images so that they can be displayed on low-dynamic-range displays and preserve as far as possible the perception of HDR. However, this may affect the visual Quality of Experience (QoE) of the end-user. QoE is a vital issue in image and video applications. It is important to understand how humans perceive quality in response to visual stimuli as this can potentially be exploited to develop and optimise image and video processing algorithms. Image consumption using mobile devices has become increasingly popular, given the availability of smartphones capable of producing and consuming HDR images along with advances in high-speed wireless communication networks. One of the most critical issues associated with mobile HDR image delivery services concerns how to maximise the QoE of the delivered content for users. An open research question therefore addresses how HDR images with different types of content perform on mobile phones. Traditionally, evaluation of the perceived quality of multimedia content is conducted using subjective opinion tests (i.e., explicitly), such as Mean Opinion Scores (MOS). However, it is difficult for the user to link the quality they are experiencing to the quality scale. Moreover, MOS does not give an insight into how the user feels at a physiological level in response to satisfaction or dissatisfaction with the perceived quality. To address this issue, measures that can be taken directly (implicitly) from the participant have now begun to attract interest. The electroencephalogram (EEG) is a promising approach that can be used to assess quality related processes implicitly. However, implicit QoE approaches are still at an early stage and further research is necessary to fully understand the nature of the recorded neural signals and their associations with user-perceived quality. Nevertheless, the EEG is expected to provide additional and complementary information that will aid understanding of the human perception of content. Furthermore, it has the potential to facilitate real-time monitoring of QoE without the need for explicit rating activities. The main aim of this project was therefore to assess the QoE of HDR images employing a physiological method and to investigate its potential application in the field of healthcare. This resulted in the following five main contributions to the research literature: 1. A detailed understanding of the relationship between the subjective and objective evaluation of the most popular TMOs used for colour and greyscale HDR images. Different mobile displays and resolutions were therefore presented under normal viewing conditions for the end-user with an LDR display as a reference. Preliminary results show that, compared to computer displays, small screen devices (SSDs) such as those used in smartphones impact the performance of TMOs in that a higher resolution gave more favourable MOS results. 2. The development of a novel Electrophysiology-based QoE assessment of HDR image quality that can be used to predict perceived image quality. This was achieved by investigating the relationships between changes in EEG features and subjective quality test scores (i.e. MOS) for HDR images viewed with SSD. 3. The development of a novel QoE prediction model, based on the above findings. The model can predict user acceptability and satisfaction for various mobile HDR image scenarios based on delta-beta coupling. Subjective quality tests were conducted to develop and evaluate the model, where the HDR image quality was predicted in terms of MOS. 4. The development of a new method of detecting a colour vision deficiency (CVD) using EEG and HDR images. The results suggest that this method may provide an accurate way to detect CVD with high sensitivity and specificity (close to 100%). Potentially, the method may facilitate the development of a low-cost tool suitable for CVD diagnosis in younger people. 5. The development of an approach that enhances the quality of dental x-ray images. This uses the concepts of QoE in HDR images without re-exposing patients to ionising radiation, thus improving patient care. Potentially, the method provides the basis for an intelligent model that accurately predicts the quality of dental images. Such a model can be embedded into a tool to automatically enhance poor quality dental images.

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