This paper presents a pixel selection method for compact image representation based on superpixel segmentation and tensor completion. Our method divides the image into several regions that capture important textures or semantics and selects a representative pixel from each region to store. We experiment with different criteria for choosing the representative pixel and find that the centroid pixel performs the best. We also propose two smooth tensor completion algorithms that can effectively reconstruct different types of images from the selected pixels. Our experiments show that our superpixel-based method achieves better results than uniform sampling for various missing ratios.

翻译：本文提出一种基于超像素分割和张量补全的紧凑图像表示像素选择方法。该方法将图像分割成若干捕获重要纹理或语义的区域，并从每个区域中选取代表性像素进行存储。我们实验了不同标准的代表性像素选择策略，发现质心像素效果最优。此外，我们提出两种平滑张量补全算法，能够从所选像素中有效重建各类图像。实验表明，在不同缺失比例下，基于超像素的方法相较于均匀采样取得了更优的重建效果。