Hundreds of millions of security cameras run around the clock. Resolutions keep climbing into HD and 4K+, channel counts keep rising, and retention rules keep stretching. The result is a storage problem that is no longer a side cost \u2014 it is one of the largest line items in video security.<\/p>\n\n\n\n
Hard-drive pricing stays volatile. Enterprise storage budgets keep going up. Conventional codecs make it worse: they treat every pixel the same. Empty pavement, a parked wall, and a person crossing the frame all get the same bitrate. Industry estimates put roughly 70% of security footage in static or low-value scenes. Uniform compression spends most of its capacity on content that will never matter in an investigation.
The next step is not a slightly better codec. It is encoding that understands the scene.<\/p>\n\n\n\n
Large-scale visual AI models can now sit inside the encoding pipeline. Instead of compressing the frame as a uniform grid, they identify regions of interest \u2014 people, vehicles, and other critical targets \u2014 and allocate quality where it counts. Those objects stay sharp. Static backgrounds take aggressive compression. Storage drops without throwing away the evidence operators actually need.<\/p>\n\n\n\n
Hikvision\u2019s Guanlan Encoding is built on that idea. It brings the company\u2019s Guanlan large-scale AI model into an H.265 pipeline so the encoder can decide, frame by frame, what deserves high definition and what does not.<\/p>\n\n\n\n
SourceSecurity.com and Hikvision have published a white paper on how this shift \u2014 from one-size-fits-all compression to intelligent, encode-on-demand video \u2014 changes both storage economics and how usable recorded video actually is.
What the numbers look like<\/p>\n\n\n\n
30\u201350% less storage. In a 2,000-channel example (1080p at 2 Mbps, 90-day retention), Guanlan Encoding cuts hard-drive count from 403 units to 202. That is roughly half the hardware, half the rack space, and half the electricity over a five-year run \u2014 without shortening retention or dropping the footage that matters.<\/p>\n\n\n\n
Detail where investigations need it. Precision ROI segmentation keeps faces, plates, clothing, and vehicle outlines in high definition. Traditional codecs often smear those same targets when they squeeze the whole scene. Downstream analytics stay viable because the objects the AI cares about were never blurred in the first place.<\/p>\n\n\n\n
HD over thinner pipes. Critical content gets priority, so operators can review high-detail video at bitrates closer to SD streams. Remote viewing and constrained networks become more practical without giving up forensic quality.<\/p>\n\n\n\n
Works with what you already have. Analog cameras can feed Guanlan Encoding DVRs without a forklift upgrade. New sites can put the intelligence at the camera. Retrofits can put it at the recorder and still capture most of the savings.<\/p>\n\n\n\n
The change is conceptual as much as technical. Recording stops being a passive, uniform dump of pixels. It becomes content-aware storage: keep what an investigator or an analytics engine will use, compress the rest.<\/p>\n\n\n\n