The application of methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D GS) have steadily gained popularity in the field of 3D object segmentation in static scenes. These approaches demonstrate efficacy in a range of 3D scene understanding and editing tasks. Nevertheless, the 4D object segmentation of dynamic scenes remains an underexplored field due to the absence of a sufficiently extensive and accurately labelled multi-view video dataset. In this paper, we present MUVOD, a new multi-view video dataset for training and evaluating object segmentation in reconstructed real-world scenarios. The 17 selected scenes, describing various indoor or outdoor activities, are collected from different sources of datasets originating from various types of camera rigs. Each scene contains a minimum of 9 views and a maximum of 46 views. We provide 7830 RGB images (30 frames per video) with their corresponding segmentation mask in 4D motion, meaning that any object of interest in the scene could be tracked across temporal frames of a given view or across different views belonging to the same camera rig. This dataset, which contains 459 instances of 73 categories, is intended as a basic benchmark for the evaluation of multi-view video segmentation methods. We also present an evaluation metric and a baseline segmentation approach to encourage and evaluate progress in this evolving field. Additionally, we propose a new benchmark for 3D object segmentation task with a subset of annotated multi-view images selected from our MUVOD dataset. This subset contains 50 objects of different conditions in different scenarios, providing a more comprehensive analysis of state-of-the-art 3D object segmentation methods. Our proposed MUVOD dataset is available athttps://volumetric-repository.labs.b-com.com/#/muvod.

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.

This paper proposes the integration of residual blocks into neural representation for videos (NeRV)-based architectures with the aim of enhancing the reconstruction of detailed patterns and high-level features. Additionally, a coding pipeline is introduced, placing the implicit neural decoder in a real-life video streaming framework. Indeed, DeepCABAC is employed for model compression, applying a quantization scheme followed by the context-adaptive binary arithmetic coding (CABAC) entropy coding algorithm, ultimately leading to bitstream generation. Our method outperforms NeRV, as well as x264 and x265, achieving BD-rate gains against NeRV: $-12.06 \%$ using PSNR and $-14.25 \%$ using MS-SSIM. Furthermore, it exhibits superior subjective quality compared to NeRV, attributed to enhanced high-level feature reconstruction. This observed behavior encourages the application of our method to other NeRV-based models, such as E-NeRV.

We present a multi-view camera and spatialized audio microphone capture system designed for computer vision applications in free navigation immersive experiences. We propose a dataset of two long and complex in-situ training situations in the medical field. The scenarios in the dataset feature precise gestures for the learner to reproduce during complex situations with multiple simultaneous visual and auditory cues important for training. 3D computer vision techniques are used to reconstruct a 4D scene model from a set of videos to render novel views from unseen viewpoints. However, the quality of the rendered objects is directly dependent on the density of coverage by reference views. To ensure maximum Quality of Experience, we propose a dual rig of cameras, a central rig that captures the details of the gesture zone of the training scenarios and a peripheral rig that captures the environment of the room and the interactions occurring around the gesture zone. The central rig provides dense coverage of the central content, facilitating high-quality reconstruction on novel views of the captured gestures. Recordings include audio interactions of multiple actors, captured by Ambisonic microphones spatially distributed around the scene. The captured scenes are real-world educational content for medical courses, so this dataset provides a rare opportunity to assess the Quality of Experience of volumetric video techniques on realistic content, and to compare their pedagogical capabilities with standard multi-view video content.

Autism spectrum disorders affect the way people perceive their environment and interact with it. Many autistic people have a passion for an object or a topic, such as a film, planes, or geography maps to name very few of them, which is called an affinity. This affinity is sometimes described as an obsession that prevents the ASD subjects to connect with the surrounding world, but it is also considered as a key to the autistic world and a way to make a connection. In this paper we investigate the specific role of affinity in the autistic person’s attention. We have conducted eye tracking experiments over 44 autistic subjects from 3 different institutions. We have shown them neutral images and images with their own affinity and recorded their gaze position. Results are not conclusive in all the 3 institutions, but in the 2 first ones we got significant differences between the 2 sets of images indicating a higher visual attention for the affinity.

People with autism spectrum disorder usually exhibit heterogeneous gaze patterns. Universal saliency prediction, which generates salient regions based on high fixations across all observers, is limited to analyzing the visual attention of autism spectrum disorders. To solve the problem, we propose a learning-based method named PSMANet to predict the personalized saliency map based on personal information. Collecting personal information and collecting large-scale datasets are challenging tasks for people with autism spectrum disorders, since they often suffer from deficits in social communication and interaction. The proposed approach introduces the image-similarity-measure based embedding to extract personal information and transfers the saliency distribution knowledge from universal saliency prediction to personalized saliency prediction. For evaluating our network, two popular metrics, Normalized Scanpath Salience (NSS) and Area Under Curve (AUC), are used. The experimental results show that it achieves good performance on the databases of people with autism spectrum disorder.

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.

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.

Although assessing post-processed medical images is still done by radiologists (rather than computers), numerous algorithms dedicated to medical image processing are developed without taking into consideration the expert's perceived quality scores. In order to evaluate these algorithms, we study in this paper four No-Reference(NR) quality assessment metrics in terms of correlation with perceived scores of experts. These scores were obtained through subjective tests conducted on ultrasound (US) livers images. Results show that one NR metric among the four evaluated performs the best for assessing the quality of US images. However, further study is needed for the development of more suitable NR metrics.

Ultrasonographic examination, either as visual inspection or quantitative analysis, is less effective than other medical imaging systems due to speckle noise. The state-of-the-art speckle reduction methods often offers an effective speckle reduction but generally they suffer from oversmoothig, blurring effect and man-made/artificial appearance. In this paper, a new Multi-Output Filter based on a Multiplicative Multiresolution Decomposition (MOF-MMD) is proposed. This multiscale based method, particularly efficient in the case of multiplicative noise, enhances distinctively three outputs: edges, texture and the global image. The multi-output filter aims at offering an enhanced images according to the features desired by radiologists. The different structures, textures and edges are filtered according to the contour image obtained by morphological operators. Finally, we compare the MOF-MMD method with two state-of-the-art speckle reduction methods in terms of speckle reduction capacity and image quality improvement. The results show that the proposed method offers an effective speckle reduction with an improvement of the image quality without blurry and over-smoothing effect.