  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.
