Explorable Decoding of Compressed Images

The ever-growing amounts of visual contents captured on a daily basis necessitate the use of lossy compression methods in order to save storage space and transmission bandwidth. While extensive research efforts are devoted to improving compression techniques, every method inevitably discards information. Especially at low bit rates, this information often corresponds to semantically meaningful visual cues, so that decompression involves significant ambiguity. In spite of this fact, existing decompression algorithms typically produce only a single output, and do not allow the viewer to explore the set of images that map to the given compressed code. Recently, explorable image restoration has been studied in the context of super-resolution. In this work, we propose to take this idea to the realm of image decompression. Specifically, we develop a novel deep-network based decoder architecture for the ubiquitous JPEG standard, which allows traversing the set of decompressed images that are consistent with the compressed input code. To allow for simple user interaction, we also develop a graphical user interface that comprises several intuitive exploration and editing tools. We exemplify our framework on graphical, medical and forensic use cases, demonstrating its wide range of potential applications.