Reducing Bit Streams

Some HD and megapixel cameras will, by default, restrict bit rates. Frequently, this is done via constant bit rate. Constant bit rate is an encoding method that keeps the bit rate at an arbitrary, fixed level. This can result in an always-high bit rate, or when restricted to a low bit rate, it can result in an image quality that will never be at its best.

Variable bit rate, on the other hand, establishes a predefined level of image quality that is maintained regardless of whether or not there is motion in a scene. Bit rates will fluctuate depending on the scene and the presence of movement.

Dynamic noise reduction (DNR) operates on the same principle as variable bit rate but with added intelligence to make smart decisions based on the presence or absence of motion in a scene. This can deliver up to 50% bit rate reduction over standard variable bit rate in scenes without motion.

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Prioritizing Compression Rates

In addition to noise reduction, region prioritization can also enable further lower bit rates. With region prioritization you are adapting the encoder compression ratio for various regions of an image. Multiple regions in a scene can be defined, each of which is assigned specific compression level parameters. An unimportant region can be set to use more compression and thus reduce bit rates, while important regions can be assigned a lower compression ratio to show more details.

Take a typical outdoor scene surrounding an entranceway, for example. Areas showing sky or the upper floors of a building could be set as unimportant for higher compression. The area surrounding the building entrance could be set as important and assigned a lower compression ratio to ensure facial characteristics and other identifying details of people or objects are captured. Next, the driveway or road next to the entrance could be set for normal compression.

By selecting important, unimportant and normal regions in a scene, and adapting the compression ratios accordingly, a lower average bit rate can be achieved. And, this can be done with-out the requirement for a constantly low compression ratio for the entire scene.

Remember, it is always possible to reduce bit rates by establishing a high level of compression or a low constant bit rate for each camera, but, normally, this will adversely affect image quality – obscuring details that may be needed for identification.

Balancing It All to Reduce Costs

Combining noise reduction and prioritizing the regions of a scene produces measurable results. The key benefit of this combination is that you get significantly lower bit rates without loss of image quality. Using these technologies, it is possible to reduce bit rates up to 50%. A lower bit rate, in turn, reduces bandwidth and storage requirements, and this can be done without nega
tively impacting your video quality.

For medium- to large-sized systems, reducing your storage requirements also translates into less physical equipment space, and power and cooling costs. And, all of this results in dramatic savings in video system costs for the end user.

Let’s use an example of a shopping mall with 200 cameras spread through-out indoor and outdoor areas of the facility. If the requirement is for 1080p HD cameras recording continuously for 12 hours each day and then recording when there is motion after the mall is closed, you’ll need nearly 70TB of storage for video streamed at 10 frames per second (fps) and retained for 30 days. Using cameras that employ DNR in this scenario could save you more than 7TB in required storage capacity. This can translate into more than $10,000 worth of cost savings depending on the storage devices being used. Additional savings could also be achieved by adding region prioritization to specific scenes to further reduce the average bit rate of those images.

Beyond simple cost savings, the reduction of required bandwidth for the video system is also an important selling point when speaking with the customer’s IT staff or consultant. Lower bit rates mean better performance on the end user’s network.

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