Image Retargeting: A Dataset and Metrics

Image retargeting adaptively resizes images for different display formats while preserving important content and perceptual quality. However, its development and evaluation remain limited by the lack of a comprehensive dataset with annotations and task-specific metrics. To address this gap, we construct a benchmark containing 5,000 input images, 20,000 instance-level annotations, and 20,000 pixel-level reference outputs. The dataset covers diverse input aspect ratios, instance counts, foreground layouts, and retargeting-sensitive content. To enable comprehensive and diagnostic evaluation, we further introduce a protocol comprising four scores. Semantic Faithfulness (SMF) and Layout Faithfulness (LOF) use the instance-level annotations to evaluate foreground preservation and reference-layout fidelity, while Scene and Texture Consistency (STC) and Structural and Visual Fidelity (SVF) assess aspects of visual quality not captured by these annotations. To the best of our knowledge, this is the first comprehensive image-retargeting benchmark to jointly provide instance-level annotations, pixel-level reference outputs, and annotation-driven evaluation metrics. Experiments on seven representative methods demonstrate that the proposed metrics agree more strongly with human judgments than existing evaluation measures. Our dataset and code will be made publicly available to the community.