Investigation Report on Universal Multimedia Access

Universal Multimedia Access (UMA) refers to the ability to access by any user to the desired multimedia content(s) over any type of network with any device from anywhere and anytime. UMA is a key framework for multimedia content delivery service using metadata. This investigation report analyzes the state-of-the-art technologies in UMA and tries to identify the key issues of UMA. The state-of-the-art in multimedia content adaptation, an overview of the standards that supports the UMA framework, potential privacy problems in UMA systems and some new UMA applications are presented in this report. This report also provides challenges that still remain to be resolved in UMA to make clear the potential key problems in UMA and determine which ones to solve

New Frontiers in Universal Multimedia Access

Universal Multimedia Access (UMA) refers to the ability to access by any user to the desired multimedia content(s) over any type of network with any device from anywhere and anytime. UMA is a key framework for multimedia content delivery service using metadata. This report consists of three parts. The first part of this report analyzes the state-of-the-art technologies in UMA, identifies the key issues and gives what are the new challenges that still remain to be resolved in UMA. The key issues in UMA include the adaptation of multimedia contents to bridge the gap between content creation and consuming, standardized metadata description that facilitates the adaptation (e.g. MPEG-7, MPEG-21 DIA, CC/PP), and UMA system designing considering its target application. The second part introduces our approach towards these challenges; how to jointly adapt multimedia contents including different modalities and balance their presentation in an optimal way. A scheme for adapting audiovisual contents and its metadata (text) to any screen is proposed to provide the best experience in browsing the desired content. The adaptation process is modeled as an optimization problem of the total value of the content provided to the user. The total content value is optimized by jointly controlling the balance between video and metadata presentation, the transformation of the video content, and the amount of the metadata to be presented. Experimental results show that the proposed adaptation scheme enables users to browse audiovisual contents with their metadata optimized to the screen size of their devices. The last part reports some potential UMA applications especially focusing on a universal access application to TV news archives as an example

Adaptive video delivery using semantics

The diffusion of network appliances such as cellular phones, personal digital assistants and hand-held computers has created the need to personalize the way media content is delivered to the end user. Moreover, recent devices, such as digital radio receivers with graphics displays, and new applications, such as intelligent visual surveillance, require novel forms of video analysis for content adaptation and summarization. To cope with these challenges, we propose an automatic method for the extraction of semantics from video, and we present a framework that exploits these semantics in order to provide adaptive video delivery. First, an algorithm that relies on motion information to extract multiple semantic video objects is proposed. The algorithm operates in two stages. In the first stage, a statistical change detector produces the segmentation of moving objects from the background. This process is robust with regard to camera noise and does not need manual tuning along a sequence or for different sequences. In the second stage, feedbacks between an object partition and a region partition are used to track individual objects along the frames. These interactions allow us to cope with multiple, deformable objects, occlusions, splitting, appearance and disappearance of objects, and complex motion. Subsequently, semantics are used to prioritize visual data in order to improve the performance of adaptive video delivery. The idea behind this approach is to organize the content so that a particular network or device does not inhibit the main content message. Specifically, we propose two new video adaptation strategies. The first strategy combines semantic analysis with a traditional frame-based video encoder. Background simplifications resulting from this approach do not penalize overall quality at low bitrates. The second strategy uses metadata to efficiently encode the main content message. The metadata-based representation of object's shape and motion suffices to convey the meaning and action of a scene when the objects are familiar. The impact of different video adaptation strategies is then quantified with subjective experiments. We ask a panel of human observers to rate the quality of adapted video sequences on a normalized scale. From these results, we further derive an objective quality metric, the semantic peak signal-to-noise ratio (SPSNR), that accounts for different image areas and for their relevance to the observer in order to reflect the focus of attention of the human visual system. At last, we determine the adaptation strategy that provides maximum value for the end user by maximizing the SPSNR for given client resources at the time of delivery. By combining semantic video analysis and adaptive delivery, the solution presented in this dissertation permits the distribution of video in complex media environments and supports a large variety of content-based applications