FIPER: Generalizable Factorized Fields for Joint Image Compression and Super-Resolution
Keywords: Factor fields, Image compression, Super-resolution
TL;DR: A novel representation that unifies image compression and super-resolution
Abstract: In this work, we propose a unified representation for Super-Resolution (SR) and Image Compression, termed **Factorized Fields**, motivated by the shared principles between these two tasks. Both SISR and Image Compression require recovering and preserving fine image details—whether by enhancing resolution or reconstructing compressed data. Unlike previous methods that mainly focus network architecture, our proposed approach utilizes a basis-coefficient decomposition to explicitly capture multi-scale visual features and structural components in images, addressing the core challenges of both tasks. We first derive our SR model, which includes Coefficient Backbone and Basis Swin Transformer for generalizable Factorized Fields. Then, to further unify these two tasks, we leverage the strong information-recovery capabilities of the trained SR modules as priors in the compression pipeline, improving both compression efficiency and detail reconstruction. Additionally, we introduce a merged-basis compression branch that consolidates shared structures, further optimizing the compression process. Extensive experiments show that our unified representation delivers state-of-the-art performance, achieving an average improvement of 204.4\% over the baseline in Super-Resolution (SR) and 156.1\% in Image Compression compared to the previous SOTA.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 895
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