Entropy And Vision

Rami Kanhouche (1)

1 CMLA - Centre de Mathématiques et de Leurs Applications

In vector quantization the number of vectors used to construct the codebook is always an undefined problem, there is always a compromise between the number of vectors and the quantity of information lost during the compression. In this text we present a minimum of Entropy principle that gives solution to this compromise and represents an Entropy point of view of signal compression in general. Also we present a new adaptive Object Quantization technique that is the same for the compression and the perception.

Mots clés: Entropy; Vector Quantization; compression; vision; perception; Object; Fractal

Domaines: Probabilités [math.PR]; Combinatoire [math.CO]; Apprentissage [cs.LG]; Mathématique discrète [cs.DM]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Base de données [cs.DB]

Rami Kanhouche. Entropy And Vision. 2006. ⟨hal-00081913v3⟩