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

基于信息瓶颈原理的速率约束视频描述符

• 批准号: 486615-2015
• 负责人: Chen, Jun
• 金额: $ 1.82万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=585707
• 关键词: rate; constrained; video; descriptor; based

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.

大多数现有的视觉分析系统是基于“压缩-然后-分析”的范例，其中的分析是从已经压缩的图像或视频序列中提取的视觉特征上执行的。为了利用移动的设备日益增长的计算能力，黑莓的一个研究小组正在开发一种智能视频处理系统，该系统采用了一种称为“分析-然后-压缩”的新范式。在这种新的范式中，特征首先从原始视觉数据中提取，然后压缩，为进一步处理、传输或存储做准备。然而，已经观察到，传统的视觉数据压缩技术在用于压缩从原始视频序列中提取的视觉特征时具有较差的性能。通过几次技术讨论，我们已经确定了这个问题的根源是面向重建的压缩和面向分析的压缩的不兼容性。该项目旨在通过设计一种基于信息的特征压缩算法来解决这个问题 瓶颈原理与传统的压缩算法，努力最大限度地减少重建失真，新的算法被设计为保留包含在压缩功能的相关信息，因此有可能实现上级性能在广泛的视觉分析任务。该项目将提供一种先进的视频处理技术，使黑莓成为开发新的视频处理和分析标准的主要参与者，目前正在由一个专门的MPEG小组对压缩描述符进行视频分析。除此之外，它还有望为黑莓和麦克马斯特大学在数据挖掘、隐私保护和健康相关信息技术领域的长期合作奠定基础。在教育方面，该项目为学生和研究人员提供了一个难得的机会， 经验，技能和知识，符合学术界和加拿大信息技术行业在大数据时代的需求。