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Operational Refinement of Computation for Multimedia Coding Systems

多媒体编码系统计算的操作细化

基本信息

批准号：
EP/F020015/1
负责人：
Yiannis Andreopoulos
金额：
$ 29.74万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2008
资助国家：
英国
起止时间：
2008 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FF020015%2F1
关键词：
Operational Refinement Computation Multimedia Coding

项目摘要

Multimedia coding systems today cannot provide seamless quality degradation under degraded system resources. For example, if one watches a video on a portable video player, or is in the middle of a very important phone call, and this is draining the system resources (battery), current systems do not allow for seamless trade-offs in visual (audio) quality vs battery life (computation). Today the user is practically facing the on/off situation of the digital world, while one would strongly opt for an analogue world, where energy or computational resources (complexity) are traded off with multimedia quality (e.g. visual or audible distortion).We propose to fundamentally alter the way conventional multimedia coding algorithms are computed based on a new paradigm that we call Operational Refinement of Computation for Multimedia Coding Systems . The key principle is based on altering the realization of multimedia coding algorithms to enable the new principle of incremental refinement of computation: under a refinement of the multimedia information (e.g. images/video/audio), the algorithm computation refines the previously-computed result thereby leading to incremental computation of the output. The incremental processing or reconstruction of the input/output multimedia signals enables three key advantages in comparison to existing systems. Firstly, complexity-distortion trade-offs can be formulated since every refinement layer improves upon the quality of the output result (reduces distortion) at the cost of additional complexity. Secondly, each refinement input/output layer typically consists of data with limited dynamic-range (e.g. single-bit precision data). Hence, the complexity of the processing tasks can be modelled more accurately in function of the source statistics. Thirdly, each refinement layer can be scheduled in a different part of the implementation architecture and the computation of all layers can be parallelized. This is expected to increase the execution speed and hardware utilization significantly.This proposal comes at an excellent time. There has been a flurry of research on novel sampling and capturing devices that merge successive-approximation based analogue-to-digital converters with image sensors at the pixel or sample level. This enables the sample-based, or bitplane-based capturing of the input multimedia data. At the same time, very recent results demonstrated that image displays enabling the incremental refinement of a large number of luminance shades without flicker are possible. This enables the incrementally-produced output to be directly consumed by the display monitor. These novel developments in circuit theory and design seem very promising in solving the capturing and display aspects for systems that process the input data incrementally.In summary, conventional systems provide an all or nothing multimedia representation; the computation cannot be interrupted arbitrarily when resources become unavailable and retrieve a meaningful approximation of the final result. Contrasting the existing paradigm, we propose to investigate, for the first time, a new category of best-effort signal processing and multimedia systems. Applications of this type of systems are in all environments where resources may bescarce or uncertain due to environmental constraints, based on user choice, or, finally, by construction. Examples are:* portable multimedia systems with limited energy resources,* resource-constrained adaptive surveillance or monitoring applications with always on features,* fault tolerant multimedia algorithm and system design, and* progressive pricing schemes and progressive upgrades for quality-upgradeable hardware.

当今的多媒体编码系统不能在降级的系统资源下提供无缝的质量降级。例如，如果一个人在便携式视频播放器上观看视频，或者正在进行非常重要的电话呼叫，并且这正在耗尽系统资源（电池），则当前系统不允许视觉（音频）质量与电池寿命（计算）的无缝折衷。今天，用户实际上面临着数字世界的开/关情况，而人们会强烈选择模拟世界，能源或计算资源（复杂性）与多媒体质量进行权衡（例如视觉或听觉失真）我们提出基于我们称为多媒体编码计算的操作细化的新范例从根本上改变传统多媒体编码算法的计算方式。系统.关键原理是基于改变多媒体编码算法的实现，以实现计算的增量细化的新原理：在多媒体信息（例如图像/视频/音频）的细化下，算法计算细化先前计算的结果，从而导致输出的增量计算。与现有系统相比，输入/输出多媒体信号的增量处理或重构实现了三个关键优点。首先，复杂度-失真权衡可以用公式表示，因为每个细化层都以额外的复杂度为代价来提高输出结果的质量（减少失真）。其次，每个细化输入/输出层通常由具有有限动态范围的数据（例如，单位精度数据）组成。因此，处理任务的复杂性可以根据源统计数据更准确地建模。第三，每个细化层可以在实现架构的不同部分中调度，并且所有层的计算可以并行化。预计这将大大提高执行速度和硬件利用率。已经对新颖的采样和捕获设备进行了一系列研究，这些设备在像素或样本级将基于逐次逼近的模数转换器与图像传感器合并。这使得能够对输入多媒体数据进行基于样本或基于位平面的捕获。与此同时，最近的结果表明，图像显示，使大量的亮度色调的增量细化没有闪烁是可能的。这使得增量产生的输出能够被显示监视器直接消耗。这些电路理论和设计的新发展似乎非常有前途的解决捕获和显示方面的系统，处理输入数据incrementally.In总结，传统的系统提供了一个全或没有多媒体表示;计算不能被任意中断时，资源变得不可用，并检索一个有意义的近似的最终结果。对比现有的范例，我们建议调查，第一次，一类新的尽力而为的信号处理和多媒体系统。这种类型的系统的应用是在所有的环境中，资源可能会由于环境约束，基于用户的选择，或最终，通过建设bescarce或不确定的。例如：* 能源有限的便携式多媒体系统，* 资源受限的自适应监视或监控应用程序，* 容错多媒体算法和系统设计，以及 * 渐进式定价方案和渐进式升级的质量可接受的硬件。