Image quality models usually include a mechanism whereby artifacts are masked by the image acting as a background. Scientific study of visual masking has followed two traditions: contrast masking and noise masking, depending primarily on whether the mask is deterministic or random. In the former tradition, masking is explained by a decrease in the effective gain of the early visual system. In the latter tradition, masking is explained by an increased variance in some internal decision variable. The masking process in image quality models is usually of the gain-control variety, derived from the contrast masking tradition. In this paper we describe a third type of masking, which I call entropy masking, that arises when the mask is deterministic but unfamiliar. Some properties and implication of entropy masking are discussed. We argue that image quality models should incorporate entropy masking, as well as contrast masking.