Rate-Distortion Theory Reveals Distinct Compression Geometries in Human vs. AI Vision

Researchers have developed a new framework using rate-distortion theory (RDT) to analyze how biological and artificial visual systems compress information. This method characterizes compression strategies by measuring the trade-off between accuracy and efficiency, summarizing each system with geometric signatures: slope, curvature, and area under the rate-distortion curve. Applying this to human psychophysical data and 18 deep vision models revealed that while both systems follow a compression principle, they occupy distinct regions in rate-distortion space, with humans exhibiting more flexible trade-offs than the brittle regimes of deep networks. AI

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• Biological Visual Systems
• Deep Vision Models
• Human Psychophysical Data
• Leyla Roksan Caglar
• Rate-Distortion Signatures

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