Which Semi-Local Visual Masking Model For Wavelet Based Image Quality Metric?

International audienceProperties and models of the Human Visual System (HVS) are the fundaments for most of efficient objective image or video quality metrics. Among HVS properties, visual masking is a sensitive issue. Many models exist in literature. Simplest models can only predict visibility threshold for very simple cue while for natural images one should consider more complex approaches such as semi-local masking. Our previous work has shown the positive impact of incorporating semi-local masking in image quality metric according to one subjective study. It is important to consolidate this work with different subjective experiments. In this paper, different visual masking models, including contrast masking and semi-local masking, are evaluated according to three subjective studies. These subjective experiments were conducted with different protocols, different types of display devices, different contents and different populations

High Dynamic Range Visual Content Compression

This thesis addresses the research questions of High Dynamic Range (HDR) visual contents compression. The HDR representations are intended to represent the actual physical value of the light rather than exposed value. The current HDR compression schemes are the extension of legacy Low Dynamic Range (LDR) compressions, by using Tone-Mapping Operators (TMO) to reduce the dynamic range of the HDR contents. However, introducing TMO increases the overall computational complexity, and it causes the temporal artifacts. Furthermore, these compression schemes fail to compress non-salient region differently than the salient region, when Human Visual System (HVS) perceives them differently. The main contribution of this thesis is to propose a novel Mapping-free visual saliency-guided HDR content compression scheme. Firstly, the relationship of Discrete Wavelet Transform (DWT) lifting steps and TMO are explored. A novel approach to compress HDR image by Joint Photographic Experts Group (JPEG) 2000 codec while backward compatible to LDR is proposed. This approach exploits the reversibility of tone mapping and scalability of DWT. Secondly, the importance of the TMO in the HDR compression is evaluated in this thesis. A mapping-free post HDR image compression based on JPEG and JPEG2000 standard codecs for current HDR image formats is proposed. This approach exploits the structure of HDR formats. It has an equivalent compression performance and the lowest computational complexity compared to the existing HDR lossy compressions (50% lower than the state-of-the-art). Finally, the shortcomings of the current HDR visual saliency models, and HDR visual saliency-guided compression are explored in this thesis. A spatial saliency model for HDR visual content outperform others by 10% for spatial visual prediction task with 70% lower computational complexity is proposed. Furthermore, the experiment suggested more than 90% temporal saliency is predicted by the proposed spatial model. Moreover, the proposed saliency model can be used to guide the HDR compression by applying different quantization factor according to the intensity of predicted saliency map

Optimizing Perceptual Quality Prediction Models for Multimedia Processing Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Wavelets and sparse methods for image reconstruction and classification in neuroimaging

This dissertation contributes to neuroimaging literature in the fields of compressed sensing magnetic resonance imaging (CS-MRI) and image-based detection of Alzheimer’s disease (AD). It consists of three main contributions, based on wavelets and sparse methods. The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI. The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI. There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks. As a whole, the methods proposed in this dissertation contribute to the work towards efficient screening for Alzheimer’s disease, by making MRI scans of the brain faster and helping to automate image analysis for AD detection. The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI. The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI. There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks. This dissertation contributes to neuroimaging literature in the fields of compressed sensing magnetic resonance imaging (CS-MRI) and image-based detection of Alzheimer’s disease (AD). It consists of three main contributions, based on wavelets and sparse methods. The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI. The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI. There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks. As a whole, the methods proposed in this dissertation contribute to the work towards efficient screening for Alzheimer’s disease, by making MRI scans of the brain faster and helping to automate image analysis for AD detection.Open Acces

Connectivity biomarkers in neurodegenerative tauopathies

The primary tauopathies are a group of neurodegenerative diseases affecting movement and cognition. In this thesis I study Progressive Supranuclear Palsy (PSP) and the Corticobasal Syndrome (CBS), two parkinsonian disorders associated with accumulation of hyperphos- phorylated and abnormally folded tau protein. I contrast these two disorders with Parkinson’s disease (PD), which is associated with the accumulation of alpha-synuclein but has a genetic association with the MAPT gene encoding tau. Understanding the tauopathies to develop effective treatments will require a better grasp of the relationships between clinical syndromes and cognitive measures and how the anatomical and neurochemical networks that underlie clinical features might be altered by disease. I investigate simple clinical biomarkers, showing that a two-minute test of verbal fluency is a potential diagnostic biomarker to distinguish between PD and PSP and that the ACE-R and its subscores could play a role in monitoring cognition over time in PD, PSP and CBS. I assess the implementation of network analysis in Functional Mag- netic Resonance Imaging (fMRI) data, introduce Maybrain software for graphical network analysis and visualisation. I go on to show an overlap between graph theory network measures and I identify three main factors underlying graph network measures of: efficiency and distance, hub characteristics, network community measures. I apply these measures in PD, PSP and the CBS. All three diseases caused a loss of functional connectivity in com- parison to the control group that was concentrated in more highly connected brain regions and in longer distance connections. In ad- dition, widely localised cognitive function of verbal fluency co-varied with the connection strength in highly connected regions across PD, PSP and CBS. To take this further, I investigated specific functional covariance networks. All three disease groups showed reduced connectivity between the basal ganglia network and other networks, and between the anterior salience network and other networks. Localised areas of increased co- variance suggest a breakdown of network boundaries which correlated with motor severity in PSP and CBS, and duration of disease in CBS. I explore the link between gene expression of the tau gene MAPT and its effects on functional connectivity showing that the expression of MAPT correlated with connection strength in highly connected hub regions that were more susceptible to a loss of connection strength in PD and PSP. I conclude by discussing how tau protein aggregates and soluble tau oligomers may explain the changes in functional brain networks. The primary tauopathies are a group of neurodegenerative diseases affecting movement and cognition. In this thesis I study Progressive Supranuclear Palsy (PSP) and the Corticobasal Syndrome (CBS), two parkinsonian disorders associated with accumulation of hyperphos- phorylated and abnormally folded tau protein. I contrast these two disorders with Parkinson’s disease (PD), which is associated with the accumulation of alpha-synuclein but has a genetic association with the MAPT gene encoding tau. Understanding the tauopathies to develop effective treatments will require a better grasp of the relationships between clinical syndromes and cognitive measures and how the anatomical and neurochemical networks that underlie clinical features might be altered by disease. I investigate simple clinical biomarkers, showing that a two-minute test of verbal fluency is a potential diagnostic biomarker to distinguish between PD and PSP and that the ACE-R and its subscores could play a role in monitoring cognition over time in PD, PSP and CBS. I assess the implementation of network analysis in Functional Mag- netic Resonance Imaging (fMRI) data, introduce Maybrain software for graphical network analysis and visualisation. I go on to show an overlap between graph theory network measures and I identify three main factors underlying graph network measures of: efficiency and distance, hub characteristics, network community measures. I apply these measures in PD, PSP and the CBS. All three diseases caused a loss of functional connectivity in com- parison to the control group that was concentrated in more highly connected brain regions and in longer distance connections. In ad- dition, widely localised cognitive function of verbal fluency co-varied with the connection strength in highly connected regions across PD, PSP and CBS. To take this further, I investigated specific functional covariance networks. All three disease groups showed reduced connectivity between the basal ganglia network and other networks, and between the anterior salience network and other networks. Localised areas of increased co- variance suggest a breakdown of network boundaries which correlated with motor severity in PSP and CBS, and duration of disease in CBS. I explore the link between gene expression of the tau gene MAPT and its effects on functional connectivity showing that the expression of MAPT correlated with connection strength in highly connected hub regions that were more susceptible to a loss of connection strength in PD and PSP. I conclude by discussing how tau protein aggregates and soluble tau oligomers may explain the changes in functional brain networks.This work was supported by the Medical Research Council (grant number G1100464) and a Sackler Scholarshi

International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts

The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the International Conference on Mathematical Analysis and Applications in Science and Engineering – ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days). Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering, industry, and technology, to discuss the development of new mathematical models, theories, and applications that contribute to the advancement of scientific knowledge and practice. Authors proposed research in topics including partial and ordinary differential equations, integer and fractional order equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization, control, probability, computational mathematics, amongst others. The conference was designed to maximize the involvement of all participants and will present the state-of- the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio