Abstract 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.

Medical images largely contribute to the diagnosis of lung diseases, especially pneumonia, an inflammation of lungs tissue. Since the emergence of COVID-19 in late 2019, medical imaging systems, notably computed tomography (CT) scans, have considerably helped in its diagnosis as well as revealing its infection severity. Serving as such an important role in clinical practice, the quality of medical images is therefore crucial for an accurate diagnosis. Denoising techniques, as a common image processing method, are being more and more used in medical imaging. However, how image denoising technique influences medical images' quality in terms of diagnostic performance still remains to be answered. In this paper, a primary study was carried out thanks to a detection task-based image quality assessment experiment, where we explored the performance of COVID-19 classifiers on both original and denoised chest CT scans. Two different denoising methods, i.e., anisotropic diffusion (AD) and total variation (TV) filters, were used. Results showed that the TV denoised model performed better than both baseline and AD denoised model, despite its less favorable mathematical image quality metrics.

Automatic facial expression recognition (FER) has a lot of potential applications. However, even if it can be beneficial for some areas, e.g. security and healthcare, several legal and ethical challenges arise. In this article, we first present such challenges related to the deployment of FER. Then, we introduce the conduct of a focus group which allowed to highlight interesting points regarding the use of FER in a medical context. Particularly, transparency, data management, diagnoses, liability, best endeavours obligation, and non-discrimination principle are debated. We finally discuss on our study’s limitations and directions for future work.

Facial expression recognition is an important and challenging task for both the computer vision and affective computing communities, and even more specifically in the context of multimedia applications, where audience understanding is of particular interest. Recent data-oriented approaches have created the need for large-scale annotated datasets. However, most existing datasets present some weaknesses, because of the collecting methods used. In order to further highlight these issues, we investigate in this work how human visual attention is deployed when performing a facial expression recognition task. To do so, we carried out several complementary experiments, using the eye-tracking technology, as well as the BubbleView metaphor, both under laboratory and crowdsourcing settings. We show significant variations in gaze patterns depending on the emotion represented, but also on the difficulty of the task, i.e., whether the emotion is correctly recognised or not. Moreover, we use these results to propose recommendations on the ways to collect label data for facial expression recognition datasets.

4th International Conference on Computer-Human Interaction Research and Applications, BUDAPEST, HONGRIE, 05-/11/2020 - 06/11/2020

Research on image quality assessment (IQA) remains limited mainly due to our incomplete knowledge about human visual perception. Existing IQA algorithms have been designed or trained with insufficient subjective data with a small degree of stimulus variability. This has led to challenges for those algorithms to handle complexity and diversity of real-world digital content. Perceptual evidence from human subjects serves as a grounding for the development of advanced IQA algorithms. It is thus critical to acquire reliable subjective data with controlled perception experiments that faithfully reflect human behavioural responses to distortions in visual signals. In this paper, we present a new study of image quality perception where subjective ratings were collected in a controlled lab environment. We investigate how quality perception is affected by a combination of different categories of images and different types and levels of distortions. The database will be made publicly available to facilitate calibration and validation of IQA algorithms.

With the development of immersive media technologies, omnidirectional video services have been launched in many fields. Conducting subjective quality evaluation research becomes a crucial step to benchmark and ensure the quality of omnidirectional video services. As omnidirectional videos record spherical visual scenes that are broader than the visual field of human eyes, the quality scores rated by different observers are based on individual spatio-temporal viewing experience. The potential spatio-temporal inconsistency between observers may impact the reliability of subjective quality evaluation and thus challenge existing experimental methodologies. In this paper, we focus on investigating the effect of spatial-temporal inconsistency on the subjective quality evaluation of omnidirectional videos. A systematic quality evaluation experiment was designed with various viewing methods involved. Experimental results showed that the spatio-temporal inconsistency has a significant impact on the reliability of subjective quality results and the impact is strongly determined by the viewing method. We intend to provide recommendations with respect to the subjective quality evaluation of omnidirectional videos.

Our previous study has shown that image distortions cause saliency distraction, and that visual saliency of a distorted image differs from that of its distortion-free reference. Being able to measure such distortion-induced saliency variation (DSV) significantly benefits algorithms for automated image quality assessment. Methods of quantifying DSV, however, remain unexplored due to the lack of a benchmark. In this paper, we build a benchmark for the measurement of DSV through a subjective study. Sixteen experts in computer vision were asked to compare saliency maps of distorted images to the corresponding saliency maps of the original images. All saliency maps were rendered from ground truth human fixations. A statistical analysis is performed to reveal the behaviours and properties of human assessment of the saliency variation. The benchmark is made publicly available to the research community.

Reliably predicting where people look in images and videos remains challenging and requires substantial eye-tracking data to be collected and analysed for various applications. In this paper, we present an eye-tracking study where twenty-eight participants viewed forty still scenes of video advertising. First, we analyse human attentional behaviour based on gaze data. Then, we evaluate to what extent a machine - saliency model - can predict human behaviour. Experimental results show that there is a significant gap between human and machine in visual saliency. The resulting eye-tracking data would benefit the development of saliency models for video advertising or other relevant applications. The eye-tracking data are made publicly available to the research community.

Telemedicine can provide timely and high-quality clinical health care from a distance, improving access to and delivery of medical services in resource-poor settings. It can also save lives in situations of emergency. In many circumstances, the success of telemedicine practice heavily relies on the transmission of medical videos over large distances. However, video communication systems are prone to distortion in visual signals, affecting the task performance and thus putting patients at risk. It is critical to understand how practitioners perceive the quality of visual media and use such knowledge to improve clinical practice in telemedicine. In this paper, we investigate the hypothesis that visual quality perception varies between clinicians who work in different practice settings. To evaluate this hypothesis, we performed a subjective experiment where French and Chinese radiologists were asked to rate the quality of ultrasound videos compressed using different compression configurations. The results show that the way the perceived quality changes with the compression configuration is consistent among the both settings studied, however, French radiologists were more bothered by the compression artifacts. The findings can help inform future studies to develop tailored telemedicine systems for specific settings or individuals.

Medical professionals are viewing an increasing number of images and videos in their clinical routine. However, various types of distortions can affect medical imaging data, and therefore impact the viewers' experienced quality and their clinical practice. Thus it is necessary to quantify this impact and understand how the viewers, i.e., medical experts, perceive the quality of (distorted) images and videos. In this paper, we present an up-to-date review of the methodologies used in the literature for the subjective quality assessment of medical images and videos and discuss their merits and drawbacks depending on the use case.

Telesurgery enables an expert surgeon to assist a remote surgeon during a surgical intervention, which benefits patient care in resource-poor settings. In reality, videos of surgical procedures are compressed and transmitted over large distances in real time and, therefore, are subject to a wide variety of distortions. These distortions degrade the quality of videos and potentially affect the performance of the surgeons. Very little work has been carried out on human perception of video quality in the context of telesurgery. In this paper, we investigate the impact of video compression on the perceived quality of surgical videos. We designed and performed a psychophysical experiment where surgeons rated the quality of surgical videos distorted with two different compression schemes at various compression ratios. Experimental results demonstrate that the impact of video content and compression strategy on the perceived quality is statistically significant.