Image Compression with Laplacian Guided Scale Space Inpainting

We present an image compression algorithm that preserves high-frequency details and information of rare occurrences. Our approach can be thought of as image inpainting in the frequency scale space. Given an image, we construct a Laplacian image pyramid, and store only the finest and coarsest levels, thereby removing the middle-frequency of the image. Using a network backbone borrowed from an image super-resolution algorithm, we train our network to hallucinate the missing middle-level Laplacian image. We introduce a novel training paradigm where we train our algorithm using only a face dataset where the faces are aligned and scaled correctly. We demonstrate that image compression learned on this restricted dataset leads to better GAN network convergence and generalization to completely different image domains. We also show that Lapacian inpainting could be simplified further with a few selective pixels as seeds.