Image Retargeting by Content-Aware Synthesis

TL;DR

This paper introduces a content-aware synthesis framework for image retargeting that selectively synthesizes textural regions and efficiently retargets non-textural regions, improving visual quality and adaptability.

Contribution

It presents a novel framework that differentiates textural and non-textural regions for tailored retargeting, including an automatic texture detection method and saliency adjustment.

Findings

Outperforms state-of-the-art retargeting techniques in visual quality.

Automatically detects multiple disjoint textural regions.

User studies confirm improved retargeting effectiveness.

Abstract

Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on content-aware synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategy since they have different natures. We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image targeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.