Objective assessment of region of interest-aware adaptive multimedia streaming quality

Adaptive multimedia streaming relies on controlled adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality

View recommendation for multi-camera demonstration-based training

While humans can effortlessly pick a view from multiple streams, automatically choosing the best view is a challenge. Choosing the best view from multi-camera streams poses a problem regarding which objective metrics should be considered. Existing works on view selection lack consensus about which metrics should be considered to select the best view. The literature on view selection describes diverse possible metrics. And strategies such as information-theoretic, instructional design, or aesthetics-motivated fail to incorporate all approaches. In this work, we postulate a strategy incorporating information-theoretic and instructional design-based objective metrics to select the best view from a set of views. Traditionally, information-theoretic measures have been used to find the goodness of a view, such as in 3D rendering. We adapted a similar measure known as the viewpoint entropy for real-world 2D images. Additionally, we incorporated similarity penalization to get a more accurate measure of the entropy of a view, which is one of the metrics for the best view selection. Since the choice of the best view is domain-dependent, we chose demonstration-based training scenarios as our use case. The limitation of our chosen scenarios is that they do not include collaborative training and solely feature a single trainer. To incorporate instructional design considerations, we included the trainer’s body pose, face, face when instructing, and hands visibility as metrics. To incorporate domain knowledge we included predetermined regions’ visibility as another metric. All of those metrics are taken into account to produce a parameterized view recommendation approach for demonstration-based training. An online study using recorded multi-camera video streams from a simulation environment was used to validate those metrics. Furthermore, the responses from the online study were used to optimize the view recommendation performance with a normalized discounted cumulative gain (NDCG) value of 0.912, which shows good performance with respect to matching user choices

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Adaptive delivery of immersive 3D multi-view video over the Internet

The increase in Internet bandwidth and the developments in 3D video technology have paved the way for the delivery of 3D Multi-View Video (MVV) over the Internet. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D video experience may well be degraded unless content-aware precautionary mechanisms and adaptation methods are deployed. In this work, a novel adaptive MVV streaming method is introduced which addresses the future generation 3D immersive MVV experiences with multi-view displays. When the user experiences network congestion, making it necessary to perform adaptation, the rate-distortion optimum set of views that are pre-determined by the server, are truncated from the delivered MVV streams. In order to maintain high Quality of Experience (QoE) service during the frequent network congestion, the proposed method involves the calculation of low-overhead additional metadata that is delivered to the client. The proposed adaptive 3D MVV streaming solution is tested using the MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. Both extensive objective and subjective evaluations are presented, showing that the proposed method provides significant quality enhancement under the adverse network conditions

Transmission adaptative de modèles 3D massifs

Avec les progrès de l'édition de modèles 3D et des techniques de reconstruction 3D, de plus en plus de modèles 3D sont disponibles et leur qualité augmente. De plus, le support de la visualisation 3D sur le web s'est standardisé ces dernières années. Un défi majeur est donc de transmettre des modèles massifs à distance et de permettre aux utilisateurs de visualiser et de naviguer dans ces environnements virtuels. Cette thèse porte sur la transmission et l'interaction de contenus 3D et propose trois contributions majeures. Tout d'abord, nous développons une interface de navigation dans une scène 3D avec des signets -- de petits objets virtuels ajoutés à la scène sur lesquels l'utilisateur peut cliquer pour atteindre facilement un emplacement recommandé. Nous décrivons une étude d'utilisateurs où les participants naviguent dans des scènes 3D avec ou sans signets. Nous montrons que les utilisateurs naviguent (et accomplissent une tâche donnée) plus rapidement en utilisant des signets. Cependant, cette navigation plus rapide a un inconvénient sur les performances de la transmission : un utilisateur qui se déplace plus rapidement dans une scène a besoin de capacités de transmission plus élevées afin de bénéficier de la même qualité de service. Cet inconvénient peut être atténué par le fait que les positions des signets sont connues à l'avance : en ordonnant les faces du modèle 3D en fonction de leur visibilité depuis un signet, on optimise la transmission et donc, on diminue la latence lorsque les utilisateurs cliquent sur les signets. Deuxièmement, nous proposons une adaptation du standard de transmission DASH (Dynamic Adaptive Streaming over HTTP), très utilisé en vidéo, à la transmission de maillages texturés 3D. Pour ce faire, nous divisons la scène en un arbre k-d où chaque cellule correspond à un adaptation set DASH. Chaque cellule est en outre divisée en segments DASH d'un nombre fixe de faces, regroupant des faces de surfaces comparables. Chaque texture est indexée dans son propre adaptation set à différentes résolutions. Toutes les métadonnées (les cellules de l'arbre k-d, les résolutions des textures, etc.) sont référencées dans un fichier XML utilisé par DASH pour indexer le contenu: le MPD (Media Presentation Description). Ainsi, notre framework hérite de la scalabilité offerte par DASH. Nous proposons ensuite des algorithmes capables d'évaluer l'utilité de chaque segment de données en fonction du point de vue du client, et des politiques de transmission qui décident des segments à télécharger. Enfin, nous étudions la mise en place de la transmission et de la navigation 3D sur les appareils mobiles. Nous intégrons des signets dans notre version 3D de DASH et proposons une version améliorée de notre client DASH qui bénéficie des signets. Une étude sur les utilisateurs montre qu'avec notre politique de chargement adaptée aux signets, les signets sont plus susceptibles d'être cliqués, ce qui améliore à la fois la qualité de service et la qualité d'expérience des utilisateur

Rate-Distortion Analysis of Multiview Coding in a DIBR Framework

Depth image based rendering techniques for multiview applications have been recently introduced for efficient view generation at arbitrary camera positions. Encoding rate control has thus to consider both texture and depth data. Due to different structures of depth and texture images and their different roles on the rendered views, distributing the available bit budget between them however requires a careful analysis. Information loss due to texture coding affects the value of pixels in synthesized views while errors in depth information lead to shift in objects or unexpected patterns at their boundaries. In this paper, we address the problem of efficient bit allocation between textures and depth data of multiview video sequences. We adopt a rate-distortion framework based on a simplified model of depth and texture images. Our model preserves the main features of depth and texture images. Unlike most recent solutions, our method permits to avoid rendering at encoding time for distortion estimation so that the encoding complexity is not augmented. In addition to this, our model is independent of the underlying inpainting method that is used at decoder. Experiments confirm our theoretical results and the efficiency of our rate allocation strategy