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GigaBench benchmark introduced for gigapixel image compression and super-resolution

By PulseAugur Editorial · [1 sources] ·

A new benchmark called GigaBench has been introduced to evaluate learnable representations (LRs) for gigapixel image compression and super-resolution. The benchmark includes a diverse set of gigapixel images from various domains and adapts existing LR methods (Implicit, Hybrid, and Explicit) to operate efficiently on consumer hardware. GigaBench systematically assesses reconstruction quality, model size, and inference times to provide guidance for future research in this area. AI

IMPACT Establishes a new standard for evaluating image compression and super-resolution techniques using learnable representations.

RANK_REASON The cluster describes a new benchmark and evaluation for learnable representations in image processing, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GigaBench benchmark introduced for gigapixel image compression and super-resolution

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The cluster describes a new benchmark and evaluation for learnable representations in image processing, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Matteo Fusconi, Alex Costanzino, Pierluigi Zama Ramirez, Luigi Di Stefano ·

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

    arXiv:2610.31644v1 Announce Type: new Abstract: Learnable Representations (LRs) can fit parametric families of functions to diverse kinds of signals. These emergent neural-native data formats are gathering interest in the scientific community due to their compactness, resolution-…

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