Complexity Optimization Strategies for Adaptive Multimedia Receivers

该项目的前提是，为资源受限的网络设备设计和实现多媒体压缩、保护和传输算法和系统需要一种完全不同的方法。其主要思想是，不是将多媒体算法视为给定的，并对实现进行反应性调整以匹配这些算法的要求，而是对算法及其参数进行主动联合优化，并对实现任务进行划分。因此，系统能够根据包括失真容限、功率、平台体系结构特征、网络行为以及关键的数据属性在内的一系列因素和约束，在速率失真复杂性空间中的点上维持操作。将开发一个全球优化框架和相关算法，考虑数据特定对上述系统因素的影响，允许对描述这些权衡的成本函数进行正式描述，然后根据不同的设备、网络和应用进行适当的优化。具体地说：1)复杂性描述方法和模型，2)结合来自解码器的反馈和系统知识以适应处理的数据感知信道解码方法，3)结合多媒体数据对错误的时间和内容变化敏感性的知识利用错误隐藏的联合信源-信道编码方法，以及4)优化资源管理。该项目的更广泛影响是在教育下一代学生和行业合作伙伴优化资源感知系统设计方面。开发形式化的方法、算法和模型将提高现有资源受限系统的多媒体性能，并为下一代多媒体压缩算法的设计提供有价值的见解，这些算法应该是复杂性可扩展的，以及应该是多媒体感知的系统设计。

This project is premised on the belief that a fundamentally different approach is necessary for designing and implementing multimedia compression, protection and transmission algorithms and systems for resource-constrained networked devices. The main idea is that instead of considering multimedia algorithms as a given and adapting the implementations reactively to match the requirements of these algorithms, proactive joint optimization of the algorithms, their parameters, and the implementation task partitioning is performed. The system is therefore able to maintain operation at a point in rate-distortion-complexity space in accordance with a constellation of factors and constraints including distortion tolerance, power, platform architecture characteristics, network behavior, and critically, data attributes. A global optimization framework and associated algorithms that consider the data-specific impact on the system factors listed above, allowing cost functions describing these tradeoffs to be formally described and then optimized as appropriate to different devices, networks and applications will be developed. Specifically: 1) complexity description methods and models, 2) data-aware channel decoding methods that combine feedback from the decoder and system knowledge to adapt the processing, 3) joint source-channel coding methods that exploit error concealment in combination with knowledge of the time- and content-varying sensitivity of multimedia data to errors, and 4) optimized resource management. The broader impact of the project is in the area of educating next generation students and industry partners in optimized resource-aware system design. Developing formal methods, algorithms and models will lead to improved multimedia performance over existing resource-constrained systems and also provide valuable insights into the design of next generation multimedia compression algorithms that should be complexity scalable, as well as system designs that should be multimedia aware.