Statistical regularities in photographic images imply that certain representations of spatial information are better than others in terms of coding efficiency. In this course, we present the information theory concepts (entropy, multi-information, correlation and negentropy) for unsupervised feature extraction or dictionary learning required in image coding. Redundancy in images and sequences is reviewed, and basic techniques for compact information representation are introduced such as vector quantization, predictive coding, and transform coding. Application of these concepts in images is the basis of DCT and Wavelet representations, which are the core of JPEG and JPEG2000.