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Self-aware distributed AV sensor and actuator networks for improved media adaptation

机译:具有自我意识的分布式AV传感器和执行器网络,可改善媒体适应性

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Most of the existing research work in the area of media adaptation is concentrated on content adaptation, transcoding and delivery mechanisms without addressing the actual input and output of multimedia data. However, it is the I/O stage of media processing that humans are concerned about. Up until now, most multimedia applications have relied on standalone I/O devices (microphone, headphones, monitor, camera) to capture or render multimedia data. This situation is about to change. Nowadays we are surrounded by a vast number of audio/video (AV) sensors and actuators. They are built into our cellular phones, PDAs, tablets, laptops, and surveillance systems. A natural idea that comes out of this fact is to combine multiple I/O devices into a distributed array of sensors and actuators. The paper shows the feasibility of this idea and shifts media adaptation research away from a single device/stream paradigm towards array multimedia processing. We demonstrate how to transform a network of off-the-shelf devices into a distributed I/O array by providing common time (with tens of microseconds precision) and 3D space coordinates (with a few centimetres precision). We also discuss the implications and potentials of self-calibrating distributed AV-sensor/actuator networks for improved media adaptation.
机译:媒体适应领域中的大多数现有研究工作都集中在内容适应,代码转换和传递机制上,而没有解决多媒体数据的实际输入和输出问题。但是,人们关注的是媒体处理的I / O阶段。到目前为止,大多数多媒体应用程序都依靠独立的I / O设备(麦克风,耳机,监视器,摄像机)来捕获或呈现多媒体数据。这种情况即将改变。如今,我们被大量的音频/视频(AV)传感器和执行器所包围。它们内置在我们的手机,PDA,平板电脑,笔记本电脑和监视系统中。从这个事实中得出的一个自然想法是将多个I / O设备组合成传感器和执行器的分布式阵列。本文展示了这种想法的可行性,并将媒体适应性研究从单一设备/流范例转变为阵列多媒体处理。我们演示了如何通过提供公共时间(精度为几十微秒)和3D空间坐标(精度为几厘米)将现有设备网络转换为分布式I / O阵列。我们还将讨论自动校准分布式AV传感器/执行器网络对改进媒体适应性的影响和潜力。

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