Preparing subjective evaluations for point cloud quality assessment using 2D or 3D planar displays typically requires rendering uncompressed videos, which demands large volumes of data storage and processing, particularly at high resolutions. This paper describes a subjective quality evaluation of point clouds using a head-mounted display (HMD), which allows the direct 3D content depiction and thus eliminates the need for video rendering. The test dataset included six static point clouds encoded with the MPEG codecs V-PCC and G-PCC, the learning-based codec RS-DLPCC, and the Draco codec. The obtained Mean Opinion Scores (MOS) were compared with two other studies using the same dataset but with different displays, notably a 2D display and a 3D stereoscopic display. The reported results indicate that the three studies are highly correlated, with Pearson and Spearman correlation coefficients above 0.9. Moreover, a multi-way ANOVA shows that changing the display does not have a significant effect on the obtained MOS.

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.

In this paper, a stability analysis of the JPEG Pleno Learning-based Point Cloud Coding Verification Model (VmUC) is performed. The codec is a deep learning-based solution that is able to compress both color and geometry. Three different training sessions were conducted using the default training set and cost function, and six point clouds were encoded/decoded with the resulting operating points for six target distortion/bitrate ratios. The VmUC performance was compared with the MPEG codecs V-PCC and G-PCC, considering three objective metrics, notably PSNR MSE D1, PSNR MSE D2, and PCQM. PSNR MSE D1 was also computed at each training epoch for the six decoded point clouds. It is concluded that the VmUC is able to outperform G-PCC and V-PCC in geometry encoding. However, it is outperformed by V-PCC in terms of color encoding, namely across all three training sessions. Furthermore, it is also shown that the codec does not present a high level of stability, changing its performance considerably with different training sessions.

The past decades have witnessed rapid growth in imaging as a major form of communication between individuals. Due to recent advances in capture, storage, delivery and display technologies, consumers demand improved perceptual quality while requiring reduced storage. In this context, research and innovation in lossy image compression have steered towards methods capable of achieving high compression ratios without compromising the perceived visual quality of images, and in some cases even enhancing the latter. Subjective visual quality assessment of images plays a fundamental role in defining quality as perceived by human observers. Although the field of image compression is constantly evolving towards efficient solutions for higher visual qualities, standardized subjective visual quality assessment protocols are still limited to those proposed in ITU-R Recommendation BT.500 and JPEG AIC standards. The number of comprehensive and in-depth studies where different protocols are compared is still insufficient. Moreover, previous works have not investigated the effectiveness of these methods on higher quality ranges, using recent image compression methods. In this paper, subjective visual scores collected from three subjective image quality assessment protocols, namely the Double Stimulus Continuous Quality Scale (DSCQS) and two test methods described in the JPEG AIC Part 2 standard, are compared between different laboratories under similar controlled conditions. The analysis of the experimental results has revealed that the DSCQS protocol is highly influenced by the quality of the reference images and experience of the subjects, while the JPEG AIC Part 2 specifications produce more stable results but are expensive and only suitable for a limited range of qualities. These emphasize the need for new robust subjective image quality assessment methodologies able to discriminate in the range of qualities generally demanded by consumers, i.e. from high to nearly visually lossless.

Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods were evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy and Bethlem Myopathy patients at different evolution stages. The hybrid model achieved the best cross-validation results, with a global accuracy of 93.8%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.

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.

The rapid adoption of MPEG-DASH is testament to its core design principles that enable the client to make the informed decision relating to media encoding representations, based on network conditions, device type and preferences. Typically, the focus has mostly been on the different video quality representations rather than audio. However, for device types with small screens, the relative bandwidth budget difference allocated to the two streams may not be that large. This is especially the case if high quality audio is used, and in this scenario, we argue that increased focus should be given to the bit rate representations for audio. Arising from this, we have designed and implemented a subjective experiment to evaluate and analyses the possible effect of using different audio quality levels. In particular, we investigate the possibility of providing reduced audio quality so as to free up bandwidth for video under certain conditions. Thus, the experiment was implemented for live music concert scenarios transmitted over mobile networks, and we suggest that the results will be of significant interest to DASH content creators when considering bandwidth tradeoff between audio and video.

DVS (dynamic voltage scaling) has been adopted as an effective technique for reducing energy consumption in mobile embedded systems. Since reducing CPU supply voltage affects maximum CPU frequency, several concerns (e.g. deadline missing) may arise when adopting DVS in systems that contain hard real-time constraints. Although several scheduling approaches have been developed to address voltage scaling in time-critical systems, inter-task relations, such as precedence and exclusion relations, have been neglected. This work presents a method for hard real-time systems scheduling with DVS considering precedence and exclusion relations. The proposed work adopts a formal model based on time Petri nets in order to find a feasible schedule using a pre-runtime approach that satisfies timing and energy constraints.