Rate distortion optimized graph partitioning for omnidirectional image coding

International audienceOmnidirectional images are spherical signals captured by cameras with 360-degree field of view. In order to be compressed using existing encoders, these signals are mapped to planar domain. A commonly used planar representation is the equirectangular one, which corresponds to a non uniform sampling pattern on the spherical surface. This particularity is not explored in traditional image compression schemes, which treat the input signal as a classical perspective image. In this work, we build a graph-based coder adapted to the spherical surface. We build a graph directly on the sphere. Then, to have computationally feasible graph transforms, we propose a rate-distortion optimized graph partitioning algorithm to achieve an effective trade-off between the distortion of the reconstructed signals, the smoothness of the signal on each subgraph, and the cost of coding the graph partitioning description. Experimental results demonstrate that our method outperforms JPEG coding of planar equirectangular images

Graph-Based Detection of Seams In 360-Degree Images

In this paper, we propose an algorithm to detect a specific kind of distortions, referred to as seams, which commonly oc- cur when a 360-degree image is represented in planar domain by projecting the sphere to a polyhedron, e.g, via the Cube Map (CM) projection, and undergoes lossy compression. The proposed algorithm exploits a graph-based representation to account for the actual sampling density of the 360-degree sig- nal in the native spherical domain. The CM image is con- sidered as a signal lying on a graph defined on the spherical surface. The spectra of the processed and the original sig- nals, computed by applying the Graph Fourier Transform, are compared to detect the seams. To test our method a dataset of compressed CM 360-degree images, annotated by experts, has been created. The performance of the proposed algorithm is compared to those achieved by baseline metrics, as well as to the same approach based on spectral comparison but ignor- ing the spherical nature of the signal. The experimental results show that the proposed method has the best performance and can successfully detect up to approximately 90% of visible seams on our dataset

Visual Distortions in 360-degree Videos.

Omnidirectional (or 360°) images and videos are emergent signals being used in many areas, such as robotics and virtual/augmented reality. In particular, for virtual reality applications, they allow an immersive experience in which the user can interactively navigate through a scene with three degrees of freedom, wearing a head-mounted display. Current approaches for capturing, processing, delivering, and displaying 360° content, however, present many open technical challenges and introduce several types of distortions in the visual signal. Some of the distortions are specific to the nature of 360° images and often differ from those encountered in classical visual communication frameworks. This paper provides a first comprehensive review of the most common visual distortions that alter 360° signals going through the different processing elements of the visual communication pipeline. While their impact on viewers' visual perception and the immersive experience at large is still unknown-thus, it is an open research topic-this review serves the purpose of proposing a taxonomy of the visual distortions that can be encountered in 360° signals. Their underlying causes in the end-to-end 360° content distribution pipeline are identified. This taxonomy is essential as a basis for comparing different processing techniques, such as visual enhancement, encoding, and streaming strategies, and allowing the effective design of new algorithms and applications. It is also a useful resource for the design of psycho-visual studies aiming to characterize human perception of 360° content in interactive and immersive applications

Association of the PHACTR1/EDN1 genetic locus with spontaneous coronary artery dissection

Background: Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndromes (ACS) afflicting predominantly younger to middle-aged women. Observational studies have reported a high prevalence of extracoronary vascular anomalies, especially fibromuscular dysplasia (FMD) and a low prevalence of coincidental cases of atherosclerosis. PHACTR1/EDN1 is a genetic risk locus for several vascular diseases, including FMD and coronary artery disease, with the putative causal noncoding variant at the rs9349379 locus acting as a potential enhancer for the endothelin-1 (EDN1) gene. Objectives: This study sought to test the association between the rs9349379 genotype and SCAD. Methods: Results from case control studies from France, United Kingdom, United States, and Australia were analyzed to test the association with SCAD risk, including age at first event, pregnancy-associated SCAD (P-SCAD), and recurrent SCAD. Results: The previously reported risk allele for FMD (rs9349379-A) was associated with a higher risk of SCAD in all studies. In a meta-analysis of 1,055 SCAD patients and 7,190 controls, the odds ratio (OR) was 1.67 (95% confidence interval [CI]: 1.50 to 1.86) per copy of rs9349379-A. In a subset of 491 SCAD patients, the OR estimate was found to be higher for the association with SCAD in patients without FMD (OR: 1.89; 95% CI: 1.53 to 2.33) than in SCAD cases with FMD (OR: 1.60; 95% CI: 1.28 to 1.99). There was no effect of genotype on age at first event, P-SCAD, or recurrence. Conclusions: The first genetic risk factor for SCAD was identified in the largest study conducted to date for this condition. This genetic link may contribute to the clinical overlap between SCAD and FMD

Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes.

OBJECTIVE: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS: We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS: Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis

Spherical clustering of users navigating 360-degree content

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on their common navigation patterns is a first direction to understand users behavior. However, classical clustering techniques fail in identifying this common paths, since they are usually focused on minimizing a simple distance metric. In this paper, we argue that minimizing the dis- tance metric does not necessarily guarantee to identify users that ex- perience similar navigation path in the VR domain. Therefore, we propose a graph-based method to identify clusters of users who are attending the same portion of the spherical content over time. The proposed solution takes into account the spherical geometry of the content and aims at clustering users based on the actual overlap of displayed content among users. Our method is tested on real VR user navigation patterns. Results show that our solution leads to clusters in which at least 85% of the content displayed by one user is shared among the other users belonging to the same cluster

Spherical clustering of users navigating 360-degree content

In Virtual Reality (VR) applications, understanding how users explore the visual content is important in order to optimize content creation and distribution, develop user-centric services, or even to detect disorders in medical applications. I