GigaBench: Benchmarking Learnable Representations for Gigapixel Image Compression and Super-Resolution
Paper recorded by Signals 4 on 2026-10-22 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-22 on arXiv · recorded by Signals 4 on 2026-10-23
Category: cs.CV · 计算机视觉 · first seen 2026-10-23
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-agnosticism and amenability to direct neural processing. On the other hand, gigapixel images are becoming increasingly widespread across a variety of scientific, cultur
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Cite this page: GigaBench: Benchmarking Learnable Representations for Gigapixel Image Compression and Super-Resolution: the #177 most recent of 756 cs.CV papers we have recorded (as of 2026-10-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/gigabench-benchmarking-learnable-representations-for-gigapixel-image-compression.html
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