2006/06/26 by Rami Kanhouche, Kanhouche, Rami
Computer Science · Mathematics · #Combinatorics (math.CO) #Computer Vision and Pattern Recognition (cs.CV) #Databases (cs.DB) #Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #FOS: Mathematics #I.2.10 Vision and Scene Understanding #Machine Learning (cs.LG) #Probability (math.PR) #cs.CV #cs.DB #cs.DM #cs.LG #math.CO #math.PR #msc:I.2.10
paper · pdf · doi:10.48550/arxiv.math/0606643
arxiv created 2006/07/18 · arxiv updated 2009/12/01
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.