CIF: Small: A Resource-Scalable Unifying Framework for Aural Signal Coding

“该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。“用于听觉信号编码的资源可扩展的统一框架该项目的目标是为听觉信号编码实现全面、统一的框架和相应的通用方法。这些挑战是由于难以捉摸的感知标准、复杂的信号结构以及信号类型、操作设置、复杂性和延迟要求的巨大多样性的组合。历史上的努力是针对狭义的信号类型和场景进行定制的，例如用于低速率语音通信的线性预测与用于存储和流式传输的基于变换的音乐编码。然而，越来越多的人认识到它们不足以处理在许多现实世界应用中遇到的异构听觉信号和网络设置，特别是最近多媒体和网络行业的主要倡议要求联合语音音频编码标准化。该研究形式化了通用听觉信号编码的权衡，并开发了一个统一的框架和方法，使异构信号和网络设置场景下的资源可伸缩编码的有效优化。该项目的主要目标是：i）开发一个统一的资源可伸缩框架，结合有效的感知失真标准，该框架覆盖听觉信号类型和网络场景的连续范围，并且在比特率、编码/解码复杂度和延迟等方面是可伸缩的; ii）在这种统一的压缩范例内的速率-（感知）失真性能限制的理论分析; iii）用于在此统一框架内的高效编码器设计和各种资源分配的一类新的通用方法和有效的优化算法。

"This award is funded under the American Recovery and Reinvestment Act of 2009(Public Law 111-5)."A Resource-Scalable Unifying Framework for Aural Signal CodingThe objective of this project is to achieve a comprehensive, unifying framework and corresponding universal methodologies for the coding of aural signals. The challenges are due to the combination of elusive perceptual criteria, complex signal structures, and great diversity in signal type, operational setting, complexity and delay requirements. Historical efforts were tailored to narrowly defined signal types and scenarios, such as linear prediction for low rate speech communication versus transform-based music coding for storage and streaming. However, there is a growing realization of their insufficiency to handle the heterogeneous aural signals and network settings encountered in many real-world applications, as evidenced in particular by recent major initiatives of the multimedia and networking industries demanding joint speech-audio coding standardization. The research formalizes the tradeoffs that underly universal aural signal coding, and develops a unifying framework and methodologies to enable efficient optimization of resource-scalable coding under heterogeneous signal and network setting scenarios. The main thrusts of the project are: i) Development of a unifying resource-scalable framework coupled with effective perceptual distortion criteria, which covers the continuous gamut of aural signal types and networking scenarios, and is scalable in bit rate, encoding/decoding complexity and delay, etc.; ii) Theoretical analysis of rate-(perceptual) distortion performance limits within such unified compression paradigms; iii) A new class of universal methodologies and effective optimization algorithms for efficient coder design and various resource allocation within this unifying framework.