Cut the storage. Not the quality.
Over the past 10 years we have seen surveillance camera technology steadily advance offering better and better image quality. Today’s cameras can feature videos with amazingly detailed resolutions in high frame rates. Wide Dynamic Range technology enables today’s cameras to capture detailed images even in scenes with complex light and advanced image sensors allow for color video even in extreme low-light conditions.
With these advances in surveillance camera technology the requirements in terms of bandwidth and storage consumption have steadily increased. This has put a lot of pressure on system operators to manage the underlying network infrastructure as well as storage resources in a smart way. After all, the best video surveillance evidence is of no value at all if the system was configured to automatically overwrite the footage before it was needed. It makes little sense to invest in high quality cameras if important details are no longer captured and footage does not provide clear evidence due to a reduced video bit rate, resolution or frame rate.
Many manufacturers claim to have intelligent compression methods that cut the bitrates by half or more – but which half? Ever since Axis developed Zipstream in 2015 the focus was not only on what to scrap but more importantly on what to save.
What makes an intelligent compression intelligent?
If the scene never changed, you could set the camera properly and configure the compression to get the best results for the defined requirements. Unfortunately, this is not realistic, so you need technologies that are dynamic and do not require repetitive configuration.
In almost all networks, cameras will have varying levels of recording activity, from the empty lobby in the middle of the night to a train station during rush hour or a mall entrance hall before Christmas. It is therefore useful to have a surveillance camera system that can adapt to these environments, not compromise the image quality and use data space intelligently, so as not to fill the system with ‘high definition nothingness’.
Take for example an empty lobby at midnight, there is very little happening yet cameras are still recording in high definition, in the highest bitrate and using a lot of bandwidth and storage. An application that would compress the least important data, in this case just about everything, could reduce bandwidth, storage and push down the bit rate very far, achieving zero in some cases. Alternatively, in the case of a busy mall entrance during Christmas season the application would be used to compress the static and unimportant parts of the image without compromising the overall quality.
If this type of scenario is too compressed the forensic value of the video will be lost. Video compression is a complex topic and of ever increasing importance in the search to minimize storage yet maximize efficiency. If it is properly used then it can preserve relevant image information, however, if misused important information can be lost. Intelligent compression methods are completely dynamic and reduced only when it is forensically defensible. Limited bit rates are essential in video surveillance.
The challenge is to recognize what’s relevant or not. The intelligent compression is just as much about cutting the bitrate as is it about allowing a high bitrate when needed. Limited bit rates are essential in video surveillance.