  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. In this work we propose the first image
decompression method to facilitate user-exploration of the diverse set of
natural images that could have given rise to the compressed input code, thus
granting users the ability to determine what could and what could not have been
there in the original scene. 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 JPEG file. To allow for simple user interaction, we develop a
graphical user interface comprising several intuitive exploration tools,
including an automatic tool for examining specific solutions of interest. We
exemplify our framework on graphical, medical and forensic use cases,
demonstrating its wide range of potential applications.
