Complexity Optimization Strategies for Adaptive Multimedia Receivers

这个项目是基于这样的信念，即一个根本不同的方法是必要的设计和实现多媒体压缩，保护和传输算法和系统的资源受限的网络设备。其主要思想是，而不是考虑多媒体算法作为一个给定的和适应的实现反应，以匹配这些算法的要求，主动联合优化的算法，它们的参数，并执行任务划分。因此，该系统能够根据包括失真容限、功率、平台架构特性、网络行为以及关键的数据属性的因素和约束的星座来维持速率-失真-复杂度空间中的点处的操作。将开发一个全局优化框架和相关算法，考虑数据对上述系统因素的特定影响，允许描述这些权衡的成本函数被正式描述，然后根据不同的设备、网络和应用进行优化。具体而言：1）复杂度描述方法和模型，2）联合收割机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源-信道编码方法，以及4）优化的资源管理。该项目更广泛的影响是在优化资源感知系统设计方面教育下一代学生和行业合作伙伴。开发正式的方法，算法和模型将导致改善现有的资源受限的系统的多媒体性能，也提供了宝贵的见解，下一代多媒体压缩算法的设计，应该是复杂性可扩展的，以及系统设计，应该是多媒体感知。

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.