Dynamically Reconfigurable Hardware Architectures for Context-Based Statistical Compression of Visual and Data Content

这项工作的目的是研究基于上下文的视频和数据内容的统计无损压缩的算法和硬件体系结构，使用动态可重新配置的硬件来支持每种数据和压缩类型的最佳建模策略。将使用静态配置的算术编码引擎来执行建模输出的熵编码。当前网络融合的趋势表明，能够提供最佳压缩比并快速适应内容性质的技术将变得越来越重要，其中视频和数据内容沿同一物理通道传输。这两个关键概念将推动这项研究工作。基于上下文的统计压缩从根本上不同于常用算法(如ZIP系列)中使用的基于词典的压缩，它被认为能够提供比这些算法更好的压缩比。然而，这只是通过复杂的软件算法实现的，这些算法需要相当大的内存容量，并且在每个字节数千个CPU周期范围内的吞吐量非常低。这意味着需要运行GHz速率的耗电奔腾4级微处理器来提供运行这些高级统计算法所需的计算能力，甚至这些CPU也很难支持远程医疗等应用程序，在这些应用程序中，静止图像、视频和科学数据需要高带宽的无损实时压缩。其他应用，如空间中的数据、视频和图像传输，需要在能源和硅高效平台上实现性能。为了满足这些应用的需求，我们提出了第一个结合基于上下文的变阶统计建模和算术编码的通用无损压缩硬件核。目前，这些技术还没有实际的硬件实现，因为还没有为可行的体系结构提出令人满意的解决方案。

The purpose of this work is to investigate algorithms and hardware architectures for context-based statistical lossless compression of visual and data content using dynamically reconfigurable hardware to support optimal modelling strategies for each data and compression type. Entropy coding of the modelling output will be performed using a statically configured arithmetic coding engine. The current trend of network convergence where visual and data content are transmitted along the same physical channel suggests a technology capable of delivering optimal compression ratios and fast adaptation to the nature of the content will become increasingly important. These are the two key concepts that will drive this research effort. Context-based statistical compression differs fundamentally from dictionary-based compression as used in popular algorithms such as the ZIP family and it is recognised as being able to offer superior compression ratios to these. However, this has been only achieved with complex software algorithms that require considerable amounts of memory capacity and have very low throughputs in the range of thousands of CPU cycles per byte. This means that power-hungry Pentium 4 class microprocessors running at GHz rates are needed to provide the required computing power to run these advanced statistical algorithms and even these CPUs will find difficult to support applications such as telemedicine where still images, video and scientific data would require lossless real-time compression with high bandwidths. Other applications such as data, video and image transmission in space require the performance to be achieved in an energy and silicon efficient platform. To achieve the demands set by these applications we propose the first universal lossless compression hardware core combining context-based variable-order statistical modelling and arithmetic coding. At present, there are no practical hardware realisations of these techniques, since no satisfactory solutions have yet been proposed for a viable architecture.