Double-Helix Vision (DH-V2): A Geometry-Based Visual Sampler for Bandwidth-Constrained Perception
Researchers have introduced Double-Helix Vision (DH-V2), a novel geometry-based visual sampling method designed for bandwidth-constrained perception. DH-V2 compresses 2D images into compact 1D signals by utilizing paired golden-ratio-inspired spiral trajectories, featuring two phase-shifted helices for biologically inspired foveation. This approach achieves significant compression ratios, such as 1,433x at 4K resolution, while preserving geometric structure and enabling rapid perception pipeline execution without neural network dependencies. The method demonstrates improved accuracy on benchmarks like CIFAR-10 and provides a robotics API for real-time spatial perception reports. AI