Beyond Fidelity: Semantic Similarity Assessment in Low-Level Image Processing
Low-level image processing has long been evaluated mainly from the perspective of visual fidelity. However, with the rise of deep learning and generative models, processed images may preserve perceptual quality while altering semantic content, making conventional Image Quality Assessment (IQA) insufficient for semantic-level assessment. In this paper, we formalize \textit{Semantic Similarity} as a new evaluation task for low-level image processing, aimed at measuring whether semantic content is preserved after processing. We further present a structured formulation of image semantics based on semantic entities and their relations, and discuss the desired properties and constraints of a va...
| Authors | Runjie Wang, Weiling Chen, Tiesong Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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