Machine learning (ML) has been producing major advances in several technological fields and can have a significant impact on media coding. However, fast progress can only happen if the ML techniques are adapted to match the true needs of compression. In this paper, we analyze why some straightforward applications of ML tools to compression do not really address its fundamental problems, which explains why they have been yielding disappointing results. From an analysis of why compression can be quite different from other ML applications, we present some new problems that are technically challenging, but that can produce more significant advances. Throughout the paper, we present examples of successful applications to video coding, discuss practical difficulties that are specific to media compression, and describe related open research problems.
