Double-Helix Vision (DH-V2): A Geometry-Based Visual Sampler for Bandwidth-Constrained Perception
arXiv:2606.14773v1 Announce Type: cross Abstract: We present Double-Helix Vision (DH), a geometry-based visual sampler that compresses 2D images into compact 1D signals using paired golden-ratio-inspired spiral trajectories. Rather than processing every pixel uniformly, DH employs two phase-shifted helices (Alpha and Beta, offset by 180 degrees) to sample the image with biologically-inspired foveation: high density at the center, sparse coverage at the periphery. At 4K resolution, DH achieves a 1,433x compression ratio (99.93% reduction) while preserving the geometric structure of the scene. T
The paper is published as research for more efficient visual processing becomes critical for scaling AI, especially in bandwidth-constrained or edge environments.
This development proposes a novel, biologically-inspired method for extreme data compression in visual perception, potentially enabling new applications and efficiencies in AI systems.
Visual perception systems may move away from uniform pixel processing towards geometry-based, foveated sampling, drastically reducing data bandwidth requirements while maintaining critical information.
- · Edge AI developers
- · Robotics
- · Computer Vision hardware manufacturers
- · Autonomous systems
- · Traditional high-bandwidth visual processing architectures
- · Systems heavily reliant on uniform high-resolution data
Reduced computational load and energy consumption for visual inputs in AI applications.
Acceleration of real-time perception for robotics, drones, and constrained IoT devices due to lower data overhead.
New classes of AI agents and autonomous systems emerge that can operate with significantly less power and processing, expanding the reach of AI into previously infeasible environments.
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Read at arXiv cs.AI