Perceptual systems continually adapt to changes in their sensory environment. Adaptation has been mainly thought of as a mechanism to exploit the spatiotemporal regularities of the sensory input in order to efficiently represent sensory information. Thus, most computational explanations for adaptation can be conceptualized as a form of Efficient coding. We propose a novel and more holistic explanation. We argue that perceptual adaptation is a process with which the perceptual system adjusts its operational regime to be best possible prepared for the future, i.e. the next sensory input. Crucially, we assume that these adjustments affect both the way the system represents sensory information (encoding) and how it interprets that information (decoding). We apply this idea in the context of a Bayesian observer model. More specifically, we propose that the perceptual system tries to predict the probability distribution from which the next sensory input is drawn. It does so by exploiting the fact that the recent stimulus history is generally a good predictor of the future and that the overall long-term stimulus distribution is stationary. We assume that this predicted probability distribution reflects the updated prior belief of the Bayesian observer. In addition, we assume that the system is adjusting its sensory representation according to the predicted future stimulus distribution via Efficient coding. Because this sensory representation directly constrains the likelihood function, we can define an optimal Bayesian observer model for any predicted distribution over the next sensory input. We demonstrate that this model framework provides a natural account of the reported adaptation after-effects for visual orientation and spatial frequency, both in terms of discrimination thresholds and biases. It also allows us to predict how these after-effects depend on the specific form of the short- and long-term input histories.