Shaymaa S Al-Juboori

Scopus Publications

Objective quality assessment of medical images and videos: review and challenges
Rafael Rodrigues, Lucie Lévêque, Jesús Gutiérrez, Houda Jebbari, Meriem Outtas, Lu Zhang, Aladine Chetouani, Shaymaa Al-Juboori, Maria G. Martini, Antonio M. G. Pinheiro
Multimedia Tools and Applications, 2025
Quality assessment is a key element for the evaluation of hardware and software involved in image and video acquisition, processing, and visualization. In the medical field, user-based quality assessment is still considered more reliable than objective methods, which allow the implementation of automated and more efficient solutions. Regardless of increasing research on this topic in the last decade, defining quality standards for medical content remains a non-trivial task, as the focus should be on the diagnostic value assessed by expert viewers rather than the perceived quality from naïve viewers, and objective quality metrics should aim at estimating the first rather than the latter. In this paper, we present a survey of methodologies used for the objective quality assessment of medical images and videos, dividing them into visual quality-based and task-based approaches. Visual quality-based methods compute a quality index directly from visual attributes, while task-based methods, being increasingly explored, measure the impact of quality impairments on the performance of a specific task. A discussion on the limitations of state-of-the-art research on this topic is also provided, along with future challenges to be addressed.
Content Characterization for Bitrate Estimation in live Video Compression and Delivery
Shaymaa Al-Juboori, Maria G. Martini
Proceedings 13th International Conference on Image Processing Theory Tools and Applications Ipta 2024, 2024
Compressing video sequences characterized by different content complexity results in different compression bi-trates for the same quality level or in different quality levels for the same bitrate; for instance it is well known that content with high spatial complexity and/or high motion requires high bitrates for compression with adequate quality. To address this, per-title optimization is used recently (e.g., by Netflix) to generate appropriate rate-quality representations for different Video on demand (VoD) content to be streamed via adaptive video streaming. However, this cannot be adopted for live video streaming as it requires encoding (multiple times) each video content. Spatial Information (SI) and Temporal Information (TI) have been often used as an indicator of video complexity, for instance for preparing and describing content for video quality assessment tests, and for rate-distortion modeling. However, it has been questioned recently if different metrics could lead to a better estimation of “compressibility” of video. In this paper we compare multiple metrics in terms of their ability to estimate “compressibility”. This supports quality-rate estimation and the possibility to create appropriate “quality ladders” (different quality representations) for adaptive live video streaming.
Quality-of-Things Based Machine Learning for the MIoT Applications
Shaymaa Al-Juboori, Ah Alnuaimi, Amulya Karaadi, Is-Haka Mkwawa, Jianwu Zhang, Lingfen Sun
Icasspw 2023 2023 IEEE International Conference on Acoustics Speech and Signal Processing Workshops Proceedings, 2023
Enhancing the Quality of Things (QoT) is urgently needed given the rapid evolution of the Multimedia Internet of Things (MIoT). One of the challenges with MIoT is Acceptable QoT. Achieving AQoT can optimize bandwidth and storage at a level that will satisfy MIoT application’s minimal requirements. Intelligent systems using Machine Learning (ML) techniques can improve the performance of MIoT applications by keeping the minimum requirements of the resources which are necessary to maintain AQoT. The aim of this study is to develop a MIoT system based on ML to provide high performance with an acceptable QoT. The Gaussian-Naive Bayes, Fine KNN, and AdaBoost ML algorithms were investigated against different video sequences with varying bitrates and network conditions. The results based on face recognition for a Ring Video Doorbell scenario showed that ML could be used on MIoT applications to achieve AQoT and significantly reduce bandwidth and storage usage.
Robust EEG-based biomarkers to detect alzheimer’s disease
Ali H. Al-Nuaimi, Marina Blūma, Shaymaa S. Al-Juboori, Chima S. Eke, Emmanuel Jammeh, Lingfen Sun, Emmanuel Ifeachor
Brain Sciences, 2021
Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).
Modeling the relationships between changes in EEG features and subjective quality of HDR images
Shaymaa Al-Juboori, Is-Haka Mkwawa, Lingfen Sun, Emmanuel Ifeachor
Advances in Telemedicine for Health Monitoring, 2020
Quality of experience (QoE) is a human-centric paradigm, which produces the blueprint of human-behavioral-states such as perception, emotion, cognition, and expectation. Recent advances in neurophysiological monitoring tools have facilitated the study of frequency, time, and location of neuronal activity to an unprecedented degree, as well as opened doors to a better understanding of human overall behavioral systems. Physiological signals, such as the electroencephalogram (EEG), have shown promise in revealing the subject's emotion or attention in quality assessment and the correlation of this with media service quality. This chapter proposes a novel objective QoE model for high dynamic range (HDR) images and is based on the relationship between objective (i.e. delta-beta coupling) and subjective measures (i.e. mean opinion score MOS). The analysis of the results indicate that the proposed QoE model has a strong correlation with MOS scores, hence can be effectively used in predicting the overall HDR image quality. An advantage of the model is that it is lightweight and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization. Future mobile providers can benefit from applying the proposed QoE-based model to optimize users' acceptability and satisfaction for different HDR image scenarios.
Impact of tone-mapping operators and viewing devices on visual quality of experience of colour and grey-scale HDR images
Shaymaa Al-Juboori, Is-Haka Mkwawa, Lingfen Sun, Emmanuel Ifeachor
Advances in Telemedicine for Health Monitoring, 2020
Tone-mapping-operators (TMOs) provide a useful means for converting high dynamic range (HDR) images to low dynamic range (LDR) images so that they can be viewed on standard displays, but this may influence the visual quality of experience (QoE) of the end-user. There is a need to understand the impact of TMOs to inform the choice of TMO algorithms for different displays, especially for small-screen-devices (SSDs) such as those used in mobile phones. This is important, as mobile devices are becoming the primary means of consuming multimedia contents. However, few studies have been undertaken to assess the impact of TMOs and viewing devices (especially SSDs) on the visual QoE of the user when using. In this chapter, we evaluate subjectively and objectively, the commonly used TMOs in different displays and resolutions for colour and grey-scale HDR images. Our results show that viewing devices have an influence on the TMOs performance, suggesting the need for a careful choice of TMO to enhance the viewing-QoE of the end-user. As expected, the higher resolution, the better HDR-image quality. Surprisingly, there was no significant difference between the Mean of Opinion Score (MOS) scores for colour and grey-scale images in SSDs. The device and TMOs affect QoE for colour and grey HDR-image equally. We found Shannon entropy (SE) to be a good objective measure of quality for colour and grey HDR images, suggesting that entropy may find use in automated HDR quality control assessment schemes, while; HDR-VDP-2 is a good objective measure for colour HDR image only.
Investigation of relationships between changes in EEG features and subjective quality of HDR images
Shaymaa Al-Juboori, Is-Haka Mkwawa, Lingfen Sun, Emmanuel Ifeachor
Proceedings IEEE International Conference on Multimedia and Expo, 2017
Quality of Experience (QoE) assessment of multimedia services is a challenging task and an understanding of how the user perceives quality at the physiological level would facilitate this. Physiological signals, such as the electroencephalogram (EEG), have shown promise in revealing the subject's emotion or attention in quality assessment and the correlation of this with media service quality. This paper investigated the relationships between changes in EEG features and subjective quality test scores (i.e. MOS) for High Dynamic Range (HDR) images viewed with a mobile device. Results show that changes in the gamma and beta bands correlated negatively with MOS, whereas positive correlations were observed in the alpha band. Coupling between activities in the delta and beta bands (i.e. positive correlation between power in the fast beta and slow delta frequency bands) is related to anxiety and dissatisfaction. Thus, the results suggest that increases in the degree of coupling are associated with decreases in HDR quality. This also suggests that in the HDR image QoE assessment, human emotions play a significant role. Potentially, these findings may be exploited in objective QoE perception modelling.
Impact of tone-mapping operators and viewing devices on visual quality of experience
Shaymaa Al-Juboori, Is-Haka Mkwawa, Lingfen Sun, Emmanuel Ifeachor
2016 IEEE International Conference on Communications Icc 2016, 2016
The development of HDR imaging is seen as an important step towards improving the visual quality of experience (QoE) of the end user in many applications. In practice, Tone-mapping operators (TMOs) provide a useful means for converting a high dynamic range (HDR) image to a low dynamic range (LDR) image in order to achieve better visualization on standard displays. Although mobile devices are becoming popular, the techniques for displaying the content of HDR images on the screens of such devices are still in the early stages. While several studies have been conducted to evaluate TMOs on conventional displays, few studies have been carried out to date to evaluate TMOs on small screen displays, such as those used in mobile devices. In this paper we evaluate, using subjective and objective methods, the most popular Tone-mapping-operators in different mobile displays and resolutions under normal viewing conditions for the end-user. Preliminary results show that small screen displays (SSDs) have an impact on the performance of TMOs compared to computer displays. In general, the larger the mobile resolution, the better the subjective results. We also found clear differences between SSDs and LDRs performances. The best TMO for mobile displays is iCAM06 and for computer displays it is Photographic Reproduction.