GigaBench: Benchmarking Learnable Representations for Gigapixel Image Compression and Super-Resolution

GigaBench is the first benchmark designed to evaluate learnable representations (LRs) for gigapixel image compression and super‑resolution. It compiles a diverse set of gigapixel images—collected, acquired, and synthetically rendered—from various domains and tests representative LR methods (Implicit, Hybrid, and Explicit) on consumer hardware. The study systematically measures reconstruction quality, model size, compression, and inference times, revealing consistent trends and offering actionable guidance for future research.

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• benchmarks