HALO: Human-Aligned End-to-end Image Retargeting with Layered Transformations
Journal:
arXiv
Published Date:
Apr 3, 2025
Abstract
Image retargeting aims to change the aspect-ratio of an image while
maintaining its content and structure with less visual artifacts. Existing
methods still generate many artifacts or fail to maintain original content or
structure. To address this, we introduce HALO, an end-to-end trainable solution
for image retargeting. Since humans are more sensitive to distortions in
salient areas than non-salient areas of an image, HALO decomposes the input
image into salient/non-salient layers and applies different wrapping fields to
different layers. To further minimize the structure distortion in the output
images, we propose perceptual structure similarity loss which measures the
structure similarity between input and output images and aligns with human
perception. Both quantitative results and a user study on the RetargetMe
dataset show that HALO achieves SOTA. Especially, our method achieves an 18.4%
higher user preference compared to the baselines on average.
