Adaptive bit rate (ABR) streaming is one enabling technology for video streaming over modern throughput-varying communication networks. A widely used ABR streaming method is to adapt the video bit rate to channel throughput by dynamically changing the video resolution. Since videos have different ratequality performances at different resolutions, such ABR strategy can achieve better rate-quality trade-off than single resolution ABR streaming. The key problem for resolution switched ABR is to work out the bit rate appropriate at each resolution. In this paper, we investigate optimal strategies to estimate this bit rate using both quantitative and subjective quality assessment. We use the design of bitrates for 2K and 4K resolutions as an example of the performance of this strategy. We introduce strategies for selecting an appropriate corpus for subjective assessment and find that at this high resolution there is good agreement between quantitative and subjective analysis. The optimal switching bit rate between 2K and 4K resolutions is 4 Mbps.

This paper introduces a new database of freely available stereo-3D content designed to facilitate research in stereo post-production. It describes the structure and content of the database and provides some details about how the material was gathered. The database includes examples of many of the scenarios characteristic to broadcast footage. Material was gathered at different locations including a studio with controlled lighting and both indoor and outdoor on-location sites with more restricted lighting control. An intended consequence of gathering the material is that the database contains examples of degradations that would be commonly present in real-world scenarios.

Abstract

This paper presents the initial findings of an investigation into automated support for fusing textual information and non-text data from different sources within the military domain. Our aim is to develop a prototype fusion capability that can categorise, fuse and present intelligence information to military officers. To date, our focus has been on text information categorisation using both existing text classification techniques (e.g. Weighted Feature Vector (WFV) classification) and machine learning algorithms based on Inductive Logic Programming (ILP) and natural language processing techniques. The algorithms have been used to automatically assign documents to a pre-existing set of categories by correlating text within the documents to text relating to classifications/categories. Once text documents are categorised according to their content, then fusion can begin. The results of initial experiments indicate that the ILP approach performs at least as well as the WFV technique, and outperforms it in one set of experiments. Both techniques have the scope for further improvements which we outline in this paper. I. INTRODUCTIONThis paper presents the initial findings of an investigation into automated support for fusing textual information and data from different sources within the military domain. The research has been conducted under the 'Integrated Text Analysis' project within the UK MOD's Data and Information Fusion (D&IF) Defence Technology Centre (DTC).There is widespread evidence that operational effectiveness is being constrained by information management challenges: anecdotal evidence suggests that the ability to semiautomatically fuse fragments of information could significantly enhance military operations. In particular, it is anticipated that the approaches investigated in this research could reduce the requirement for human intervention in the collation and analysis of intelligence source information, enabling faster assimilation and exploitation of operational intelligence. The main aim of the DTC project is to demonstrate the utility of text fusion techniques in the military intelligence domain. This will be achieved through the development of a fusion prototype demon-

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