Manuela Pereira

Factors Influencing Quality of Experience

In this chapter different factors that may influence Quality of Experience (QoE) in the context of media consumption, networked services, and other electronic communication services and applications, are discussed. QoE can be subject to a range of complex and strongly interrelated factors, falling into three categories: human, system and context influence factors (IFs). With respect to Human IFs, we discuss variant and stable factors that may potentially bear an influence on QoE, either for low-level (bottom-up) or higher-level (top-down) cognitive processing. System IFs are classified into four distinct categories, namely content-, media-, network- and device-related IFs. Finally, the broad category of possible Context IFs is decomposed into factors linked to the physical, temporal, social, economic, task and technical information context. The overview given here illustrates the complexity of QoE and the broad range of aspects that potentially have a major influence on it.

Detection and classification of peer-to-peer traffic: A survey

The emergence of new Internet paradigms has changed the common properties of network data, increasing the bandwidth consumption and balancing traffic in both directions. These facts raise important challenges, making it necessary to devise effective solutions for managing network traffic. Since traditional methods are rather ineffective and easily bypassed, particular attention has been paid to the development of new approaches for traffic classification. This article surveys the studies on peer-to-peer traffic detection and classification, making an extended review of the literature. Furthermore, it provides a comprehensive analysis of the concepts and strategies for network monitoring.

HDR image compression: A new challenge for objective quality metrics

High Dynamic Range (HDR) imaging is able to capture a wide range of luminance values, closer to what the human visual system can perceive. It is believed by many that HDR is a technology that will revolutionize TV and cinema industry similar to how color television did. However, the complexity of HDR requires reinvention of the whole chain from capture to display. In this paper, HDR images compressed with the upcoming JPEG XT HDR image coding standard are used to investigate the correlation between thirteen well known full-reference metrics and perceived quality of HDR content. The metrics are benchmarked using ground truth subjective scores collected during quality evaluations performed on a Dolby Pulsar HDR monitor. Results demonstrate that objective quality assessment of HDR image compression is challenging. Most of the tested metrics, with exceptions of HDR-VDP-2 and FSIM computed for luma component, poorly predict human perception of visual quality.

3D Lacunarity in Multifractal Analysis of Breast Tumor Lesions in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Dynamic contrast-enhanced magnetic resonance (DCE-MR) of the breast is especially robust for the diagnosis of cancer in high-risk women due to its high sensitivity. Its specificity may be, however, compromised since several benign masses take up contrast agent as malignant lesions do. In this paper, we propose a novel method of 3D multifractal analysis to characterize the spatial complexity (spatial arrangement of texture) of breast tumors at multiple scales. Self-similar properties are extracted from the estimation of the multifractal scaling exponent for each clinical case, using lacunarity as the multifractal measure. These properties include several descriptors of the multifractal spectra reflecting the morphology and internal spatial structure of the enhanced lesions relatively to normal tissue. The results suggest that the combined multifractal characteristics can be effective to distinguish benign and malignant findings, judged by the performance of the support vector machine classification method evaluated by receiver operating characteristics with an area under the curve of 0.96. In addition, this paper confirms the presence of multifractality in DCE-MR volumes of the breast, whereby multiple degrees of self-similarity prevail at multiple scales. The proposed feature extraction and classification method have the potential to complement the interpretation of the radiologists and supply a computer-aided diagnosis system.

Analysis of Peer-to-Peer Traffic Using a Behavioural Method Based on Entropy

The increasing number of applications offering their services over peer-to-peer (P2P) platforms is changing the properties of the traffic within computer networks. Their massive use raises a few imperative challenges for network administrators and Internet service providers, regarding the quality of service and security of their networks. It such scenario, it is important to develop mechanisms to control and efficiently manage the P2P traffic and prepare the networks to support it, for which it is necessary to study the effect of P2P applications in the traffic of computer networks and to develop methodologies to characterise its behaviour. In this paper, the characteristics of the traffic generated by P2P applications are analysed from the behavioural point of view, and entropy is used to measure the heterogeneity embedded in the packet sizes. The results obtained show evident difference between P2P and non-P2P traffic, being the proposed approach applicable to real-time and high-speed networks with encrypted P2P traffic, where the existing methodologies are useless.

Comparative analysis of autofocus functions in digital in-line phase-shifting holography

Numerical reconstruction of digital holograms relies on a precise knowledge of the original object position. However, there are a number of relevant applications where this parameter is not known in advance and an efficient autofocusing method is required. This paper addresses the problem of finding optimal focusing methods for use in reconstruction of digital holograms of macroscopic amplitude and phase objects, using digital in-line phase-shifting holography in transmission mode. Fifteen autofocus measures, including spatial-, spectral-, and sparsity-based methods, were evaluated for both synthetic and experimental holograms. The Fresnel transform and the angular spectrum reconstruction methods were compared. Evaluation criteria included unimodality, accuracy, resolution, and computational cost. Autofocusing under angular spectrum propagation tends to perform better with respect to accuracy and unimodality criteria. Phase objects are, generally, more difficult to focus than amplitude objects. The normalized variance, the standard correlation, and the Tenenbaum gradient are the most reliable spatial-based metrics, combining computational efficiency with good accuracy and resolution. A good trade-off between focus performance and computational cost was found for the Fresnelet sparsity method.

A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis.

Breast ultrasound images have several attractive properties that make them an interesting tool in breast cancer detection. However, their intrinsic high noise rate and low contrast turn mass detection and segmentation into a challenging task. In this article, a fully automated two-stage breast mass segmentation approach is proposed. In the initial stage, ultrasound images are segmented using support vector machine or discriminant analysis pixel classification with a multiresolution pixel descriptor. The features are extracted using non-linear diffusion, bandpass filtering and scale-variant mean curvature measures. A set of heuristic rules complement the initial segmentation stage, selecting the region of interest in a fully automated manner. In the second segmentation stage, refined segmentation of the area retrieved in the first stage is attempted, using two different techniques. The AdaBoost algorithm uses a descriptor based on scale-variant curvature measures and non-linear diffusion of the original image at lower scales, to improve the spatial accuracy of the ROI. Active contours use the segmentation results from the first stage as initial contours. Results for both proposed segmentation paths were promising, with normalized Dice similarity coefficients of 0.824 for AdaBoost and 0.813 for active contours. Recall rates were 79.6% for AdaBoost and 77.8% for active contours, whereas the precision rate was 89.3% for both methods.

Towards the detection of microcalcifications on mammograms through Multifractal Detrended Fluctuation Analysis

In this paper, we propose a method based on a generalization of the multifractal detrended fluctuation analysis (MF-DFA) to the two-dimensionality, for the analysis of breast medical images, particularly grey scale images of mammograms. The existent generalization has been suitably applied in synthetic multifractal surfaces. However, when it is applied to natural images only some scaling laws are revealed, not reinforcing the relevance of the method in the field. Therefore, it is shown that the proposed method is appropriate for detecting signs of breast cancer in mammograms, such as microcalcifications, based on recent approaches of the self-similarity formalism. The accuracy of the method is analysed and discussed when applied to mammograms, revealing features distinguished by direct singularity information extraction.

Classification of Breast Masses on Contrast-Enhanced Magnetic Resonance Images Through Log Detrended Fluctuation Cumulant-Based Multifractal Analysis

This paper proposes a multiscale automated model for the classification of suspicious malignancy of breast masses, through log detrended fluctuation cumulant-based multifractal analysis of images acquired by dynamic contrast-enhanced magnetic resonance. Features for classification are extracted by computing the multifractal scaling exponent for each of the 70 clinical cases and by quantifying the log-cumulants reflecting multifractal information related with texture of the enhanced lesions. The output is compared with the radiologist diagnosis that follows the Breast Imaging-Reporting and Data System (BI-RADS). The results suggest that the log-cumulant C2 can be effective in classifying typically biopsy-recommended cases. The performance of a supervised classification was evaluated by receiver operating characteristic (ROC) with an area under the curve of 0.985. The proposed multifractal analysis can contribute to novel feature classification techniques to aid radiologists every time there is a change in the clinical course, namely, when biopsy should be considered.

Identification of Peer-to-Peer VoIP Sessions Using Entropy and Codec Properties

Voice over Internet Protocol (VoIP) applications based on peer-to-peer (P2P) communications have been experiencing considerable growth in terms of number of users. To overcome filtering policies or protect the privacy of their users, most of these applications implement mechanisms such as protocol obfuscation or payload encryption that avoid the inspection of their traffic, making it difficult to identify its nature. The incapacity to determine the application that is responsible for a certain flow raises challenges for the effective management of the network. In this paper, a new method for the identification of VoIP sessions is presented. The proposed mechanism classifies the flows, in real-time, based on the speech codec used in the session. To make the classification lightweight, the behavioral signatures for each analyzed codec were created using only the lengths of the packets. Unlike most previous approaches, the classifier does not use the lengths of the packets individually. Instead, it explores their level of heterogeneity in real time, using entropy to emphasize such feature. The results of the performance evaluation show that the proposed method is able to identify VoIP sessions accurately and simultaneously recognize the used speech codec.