Measuring meaningful information in images: algorithmic specified complexity

Both Shannon and Kolmogorov-Chaitin-Solomonoff (KCS) information models fail to measure meaningful information in images. Pictures of a cow and correlated noise can both have the same Shannon and KCS information, but only the image of the cow has meaning. The application of `algorithmic specified complexity´ (ASC) to the problem of distinguishing random images, simple images and content-filled images is explored. ASC is a model for measuring meaning using conditional KCS complexity. The ASC of various images given a context of a library of related images is calculated. The `portable network graphic´ (PNG) file format´s compression is used to account for typical redundancies found in images. Images which containing content can thereby be distinguished from those containing simply redundancies, meaningless or random noise.