The Musical Slide Show Multimedia Application Format (MAF) which is currently being standardized by the Moving
Picture Expert Group (MPEG) conveys the concept of combining several established standard technologies in a single
file format. It defines the format of packing up MP3 audio data, along with JPEG images, MPEG-7 Simple Profile
metadata, timed text, and MPEG-4 LASeR script. The presentation of Musical Slide Show MAF contents is made in a
synchronized manner with JPEG images, timed text to MP3 audio track. Also, the rendering effect on JPEG images can
be supported by the MPEG-4 LASeR script. This Musical Slide Show MAF will enrich the consumption of MP3
contents assisted with synchronized and rendered JPEG images, text as well as MPEG-7 metadata about the MP3 audio
contents. However, there is no protection and governance mechanism for Musical Slide Show MAF which is the
essential elements to deploy the sorts of contents. In this paper, to manage the Musical Slide Show MAF contents in a
controlled manner, we present a protection and governance mechanism by using MPEG-21 Intellectual Property
Management and Protection (IPMP) Components and MPEG-21 Rights Expression Language (REL) technologies We
implement an authoring tool and a player tool for Musical Slide Show MAF contents and show the experimental results
Much research has been made to make it possible the ubiquitous video services over various kinds of user information terminals anytime and anywhere. In this paper, we design a prototype system for the seamless TV program content consumption based on user preference via various kinds of user information terminals in digital home environment, and we show an implementation and testing results with the prototype system. The prototype system operates with the TV Anytime metadata for the consumption of TV program contents based on user preferences in TV program genres, and use the MPEG-21 DIA (Digital Item Adaptation) tools which are the representation schema formats in order to describe the context information for user environments, user terminal characteristics, user characteristics for universal access and consumption of the preferred TV program contents. The proposed content mobility prototype system supports one or more users to seamlessly consume the same TV program contents via various kinds of user terminals. The proposed content mobility prototype system consists of a home server, display TV terminals, and user information terminals. We use 42 TV programs contents in eight different genres from four different TV channels in order to test our proposed prototype system.
We introduce a novel model capturing user preference using the Bayesian approach for recommending users' preferred multimedia content. Unlike other preference models, our method traces the trend of a user preference in time. It allows us to do online learning so we do not need exhaustive data collection. The tracing of the trend can be done by modifying the frequency of attributes in order to force the old preference to be correlated with the current preference under the assumption that the current preference is correlated with the near future preference. The modification is done by partitioning usage history data into smaller sets in a time axis and then weighting the frequencies of attributes to be computed from the partitioned sets of the usage history data in order to differently reflect their significance on predicting the future preference. In the experimental section, the learning and reasoning on user preference in genres are performed by the proposed method with a set of real TV viewers' watching history data collected from many real households. The reasoning performance by the proposed method is also compared with that by a typical method without training in order to show the superiority of our proposed method.
Telematics, a compound word with Telecommunications and Informatics, represents a kind of information service which provides traffic, public transport and emergency information to automobile drivers by using car navigation or other interactive communication system. In particular, as the DAB (Digital Audio Broadcasting) or DMB (Digital Multimedia Broadcasting) technology is introduced and commercialized, telematics is rapidly converging with various applications such as broadcasting and communication services.
In this paper, we suggest an idea how a telematics service can be realized by DMB application which enables multimedia service operate on mobile devices. In order to achieve this goal, we generate multimedia content including TPEG (Transport Protocol Experts Group) contents which contain information about road and traffic. TPEG is an expert group which aims at defining a byte-oriented protocol for transport information broadcast. Transport information includes Road Traffic Messages, Public Transport Information and Location information which enables safe and efficient driving for drivers. In Europe, TPEG contents were delivered over DAB network which supports audio only broadcasting. We investigate the technique to deliver the multimedia content with TPEG content over DMB network so that we can provide the information in the scope of telematics as well as multimedia contents.
In this paper, we introduce a new supervised learning method of a Bayesian network for user preference models. Unlike other preference models, our method traces the trend of a user preference as time passes. It allows us to do online learning so we do not need the exhaustive data collection. The tracing of the trend can be done by modifying the frequency of attributes in order to force the old preference to be correlated with the current preference under the assumption that the current preference is correlated with the near future preference. The objective of our learning method is to force the mutual information to be reinforced by modifying the frequency of the attributes in the old preference by providing weights to the attributes. With developing mathematical derivation of our learning method, experimental results on the learning and reasoning performance on TV genre preference using a real set of TV program watching history data.
Traditional transcoding on multimedia has been performed from the perspectives of user terminal capabilities such as display sizes and decoding processing power, and network resources such as available network bandwidth and quality of services (QoS) etc. The adaptation (or transcoding) of multimedia contents to given such constraints has been made by frame dropping and resizing of audiovisual, as well as reduction of SNR (Signal-to-Noise Ratio) values by saving the resulting bitrates. Not only such traditional transcoding is performed from the perspective of user’s environment, but also we incorporate a method of semantic transcoding of audiovisual based on region of interest (ROI) from user’s perspective. Users can designate their interested parts in images or video so that the corresponding video contents can be adapted focused on the user’s ROI. We incorporate the MPEG-21 DIA (Digital Item Adaptation) framework in which such semantic information of the user’s ROI is represented and delivered to the content provider side as XDI (context digital item). Representation schema of our semantic information of the user’s ROI has been adopted in MPEG-21 DIA Adaptation Model. In this paper, we present the usage of semantic information of user’s ROI for transcoding and show our system implementation with experimental results.