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Measuring meaningful information in images: algorithmic specified complexity

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PROJECT TITLE :

Measuring meaningful information in images: algorithmic specified complexity

ABSTRACT:

Both Shannon and Kolmogorov–Chaitin–Solomonoff (KCS) data models fail to measure meaningful info in pictures. Footage of a cow and correlated noise can each have the same Shannon and KCS info, however solely a twin of the cow has that means. The appliance of 'algorithmic specified complexity’ (ASC) to the matter of distinguishing random images, straightforward pictures and content-filled images is explored. ASC may be a model for measuring that means using conditional KCS complexity. The ASC of various images given a context of a library of connected pictures is calculated. The 'moveable 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.


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Measuring meaningful information in images: algorithmic specified complexity - 4.9 out of 5 based on 78 votes

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