Information Theory and Its Application to Image Coding

Image compression is concerned with reducing the amount of data needed to represent an image. Efficient image representation is achieved by exploiting the statistical and psychovisual redundancies of an image. This reported focusses on the main principles of information theory, which provides a framework for efficient signal coding from a statistical perspective. Two of the fundamental theories are noiseless coding theory and rate-distortion or noisy coding theory. Noiseless source coding theorem is introduced and its application to entropy coding discussed. The widely used entropy coders, Huffman and arithmetic coding, are examined. Rate-distortion theory is examined in the subsequent section. In particular, the discussion examines how rate-distortion theory and high-rate quantisation theory are used to establish performance bounds for scalar and vector quantisers. Recent rate-distortion theories applied to vector quantisation are discussed.