Prune and Repaint: Content-Aware Image Retargeting for any Ratio
Feihong Shen, Chao Li, Yifeng Geng, Yongjian Deng, Hao Chen

TL;DR
PruneRepaint is a content-aware image retargeting method that adaptively prunes and repaints regions to preserve key semantics and aesthetics across various aspect ratios, reducing artifacts and deformation.
Contribution
It introduces a novel semantic importance-guided pruning and adaptive repainting framework for improved content-aware image retargeting.
Findings
Outperforms previous methods in preserving semantics and aesthetics.
Demonstrates better generalization across diverse aspect ratios.
Validated through objective metrics and user studies.
Abstract
Image retargeting is the task of adjusting the aspect ratio of images to suit different display devices or presentation environments. However, existing retargeting methods often struggle to balance the preservation of key semantics and image quality, resulting in either deformation or loss of important objects, or the introduction of local artifacts such as discontinuous pixels and inconsistent regenerated content. To address these issues, we propose a content-aware retargeting method called PruneRepaint. It incorporates semantic importance for each pixel to guide the identification of regions that need to be pruned or preserved in order to maintain key semantics. Additionally, we introduce an adaptive repainting module that selects image regions for repainting based on the distribution of pruned pixels and the proportion between foreground size and target aspect ratio, thus achieving…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Video Analysis and Summarization
