论文标题
快速混合图像重新定位
Fast Hybrid Image Retargeting
论文作者
论文摘要
图像重新定位会改变图像的纵横比，同时旨在保留内容并最大程度地减少明显的失真。由于图像和显示纵横比的种类繁多，目前快速和高质量的方法目前特别相关。我们提出了一种重新定位方法，该方法通过使用内容感知的种植来量化和限制扭曲扭曲。提议的方法的管道包括以下步骤。首先，使用深层语义分割和显着检测模型生成源图像的重要性图。然后，使用轴对齐变形计算初步翘曲网格，通过使用失真度量来确保翘曲变形较低。最后，使用内容感知的种植算法生成重新定位的图像。为了评估我们的方法，我们根据RetargetMe基准进行了用户研究。实验分析表明，我们的方法在其执行时间的一小部分时都优于最近的方法。
Image retargeting changes the aspect ratio of images while aiming to preserve content and minimise noticeable distortion. Fast and high-quality methods are particularly relevant at present, due to the large variety of image and display aspect ratios. We propose a retargeting method that quantifies and limits warping distortions with the use of content-aware cropping. The pipeline of the proposed approach consists of the following steps. First, an importance map of a source image is generated using deep semantic segmentation and saliency detection models. Then, a preliminary warping mesh is computed using axis aligned deformations, enhanced with the use of a distortion measure to ensure low warping deformations. Finally, the retargeted image is produced using a content-aware cropping algorithm. In order to evaluate our method, we perform a user study based on the RetargetMe benchmark. Experimental analyses show that our method outperforms recent approaches, while running in a fraction of their execution time.
