Measuring meaningful information in images: algorithmic specified complexity

Abstract

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.

Keywords

• meaningful information measurement
• algorithmic specified complexity
• Shannon information model
• Kolmogorov-Chaitin-Solomonoff information model
• correlated noise
• ASC