A rate-constrained video descriptor based on the information bottleneck principle

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Most existing visual analysis systems are based on the "Compress-Then-Analyze" paradigm, where the analysis is performed on visual features extracted from the already-compressed images or video sequences. In order to exploit the increasing computational capabilities of mobile units, a research team at BlackBerry is developing a smart video processing system according to a new paradigm known as "Analyze-Then-Compress". In this new paradigm, features are first extracted from the raw visual data and then compressed in preparation for further processing, transmission or storage. However, it has been observed that the legacy visual data compression techniques have poor performance when used to compress the visual features extracted from the raw video sequences. Through several technical discussions, we have identified that the source of this problem is the incompatibility of reconstruction-oriented compression and analysis-oriented compression. The proposed project aims to tackle this problem by designing a feature compression algorithm based on the information bottleneck principle. In contrast to the conventional compression algorithms that strive to minimize the reconstruction distortion, the new algorithm is designed to preserve the relevant information contained in the compressed features and consequently has the potential to achieve superior performance in a wide range of visual analysis tasks. This project will provide a cutting-edge video processing technology that will enable BlackBerry to become a major player in developing the new video processing and analysis standard, currently being explored by an ad-hoc MPEG group on Compact Descriptors for Video Analysis. In addition to that, it is expected to build the foundation of a long-term collaboration between BlackBerry and McMaster University in the areas of data mining, privacy protection, and health-related information technologies. On the educational front, the project provides an extraordinary opportunity for students and researchers to acquire valuable experience, skills, and knowledge that are in line with the needs of academia and Canadian information technology industries in the big data era.