The shift toward intelligent video encoding marks a departure from one-size-fits-all compression. Hikvision’s Guanlan Encoding utilizes large-scale visual AI models to perform selective compression based on regions of interest. By identifying and preserving high-definition details for dynamic subjects like people and vehicles while applying aggressive compression to static environments, this method optimizes hardware resources without compromising critical data.
Hikvision pivots to AI-driven semantic video encoding
With global security networks scaling to 4K resolutions and longer retention mandates, the industry faces an unsustainable surge in storage costs. Traditional uniform compression now struggles to manage the influx, as roughly 70% of footage consists of static backgrounds that consume the bulk of enterprise storage budgets.
A new white paper, produced in collaboration with SourceSecurity.com, highlights the operational impact of this technology. For a standard 2,000-channel project requiring 90-day retention, the shift to Guanlan Encoding can reduce hard drive requirements by 50%. Beyond hardware savings, the approach preserves image clarity for downstream AI analytics, addressing the degradation often seen in conventional systems. Furthermore, the technology enables the transmission of high-definition quality over standard-definition bandwidth, ensuring remote accessibility on constrained networks. Deployment remains flexible, as the encoding works with both existing analog infrastructure and new AI-enabled front-end cameras.
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