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Bandwidth Management

Now consider surveillance. The first issue is that video streams need to arrive in a timely fashion. There is nothing of benefit if an operator becomes aware of an incident after it has happened. Indeed, such an eventuality might even create more problems due to a false sense of security , along with liability issues if a serious event is missed. Slowing the flow of data to increase the transmission timescale doesn’t offer a credible method of bandwidth management for surveillance. Many network management techniques have evolved with typical datahandling applications in mind. These work on the basis that when a file is updated and saved, it is transmitted from the relevant node. At other times, no data is being moved across the network from that specific node. Back-ups are scheduled for periods when the network isn’t in general use. However, with video surveillance, the nodes – in this case cameras or encoders – are constantly producing files, sometimes at a rate of 25 files every second! These files are continuously transmitted, from multiple nodes. Even a small transmission delay would very quickly grow to the point that the video would be compromised. Therefore, when considering bandwidth management for video surveillance, the only credible option is to manage the amount of data being transmitted.

Reducing the load Reducing the amount of data produced by a video surveillance system doesn’t necessarially mean that the information must be ‘thrown away’. There are two approaches. One is to reduce the total amount of data on the network, and the other is to localise the data handling. With the former option, information will inevitably be lost. The latter option allows all of the data to be stored securely, but only low resolution files are streamed. If an operator needs more detail, then and only then will they bring back the bandwidth-heavy footage. Modern compression techniques have helped to reduce bandwidth requirements. However, there are a few issues with compression. Some manufacturers make claims such as reductions of video frames to less than 10Kb. Such figures are attainable, but the video quality will be badly affected. Given that one of the reasons for moving to networked video is higher resolutions, such

an approach makes no sense! As an aside, it should be remembered that file sizes are measured in bytes, while bandwidth is measured in bits. One byte is made up of eight bits. The standard methods of reducing data file sizes include reducing the frame rate and lower resolutions. There are issues with both approaches. With frame rate reduction, it is important that the end user is aware of the situation. For example, if they have purchased an HD solution, then any reduction in frame rate will mean that the system is technically no longer delivering HD streams! Equally, where the reason for a system upgrade is to achieve enhanced quality, then a resolution reduction may be counter-productive. With the latest compression algorithms, there is a possibility to reduce the GOV (group of video) length. Also often referred to as GOP (group of pictures), this adjusts the frequency of the capture of full I-Frames. The I-Frames are used as a reference when creating the predictive P-Frames, and are more bandwidthheavy. Care needs to be taken when adjusting the GOV length, as long periods without a reference frame can lead to some unexpected artefacts which could call the credibility of evidential video into question. In many applications, a better understanding of how compression works can facilitate reductions in bandwidth requirements. For example, with predictive compression algorithms, an image with noise caused by low light conditions will require more bandwidth. Even in a constant bit-rate system, the image quality will fall due to larger file sizes. This is because the compression engine ‘sees’ the noise as motion. Therefore it uses less of the detail from the reference I-Frame, and captures (and transmits) more data for the P-Frame. In general, the cleaner, sharper and brighter the viewed scene and resulting image, the less bandwidth will be required to deliver an image of an acceptable quality. Simply applying best practice can deliver real-world bandwidth savings.

Next issue... The theory discussed is all well and good, but what is the real cost of bandwidth reduction? Benchmark will test systems in a variety of configurations to see how the various methods actually impact on the bandwidth requirements for a surveillance solution.


Benchmark April 2013  

Benchmark April 2013