TY - JOUR
T1 - How complex is a random picture?
AU - Aurzada, Frank
AU - Lifshits, Mikhail
PY - 2019/8
Y1 - 2019/8
N2 - We study the amount of information that is contained in “random pictures” by which we mean the sample sets of a Boolean model. To quantify the notion “amount of information” two closely connected questions are investigated: on the one hand, we study the probability that a large number of balls is needed for a full reconstruction of a Boolean model sample set. On the other hand, we study the quantization error of the Boolean model w.r.t. the Hausdorff distance as a distortion measure.
AB - We study the amount of information that is contained in “random pictures” by which we mean the sample sets of a Boolean model. To quantify the notion “amount of information” two closely connected questions are investigated: on the one hand, we study the probability that a large number of balls is needed for a full reconstruction of a Boolean model sample set. On the other hand, we study the quantization error of the Boolean model w.r.t. the Hausdorff distance as a distortion measure.
KW - Boolean model
KW - Functional quantization
KW - High resolution quantization
KW - Information based complexity
KW - Metric entropy
KW - DIFFUSION-PROCESSES
KW - CODING COMPLEXITY
KW - QUADRATURE
KW - FUNCTIONAL QUANTIZATION
KW - CONSTRUCTIVE QUANTIZATION
UR - http://www.scopus.com/inward/record.url?scp=85057790702&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/complex-random-picture
U2 - 10.1016/j.jco.2018.11.003
DO - 10.1016/j.jco.2018.11.003
M3 - Article
AN - SCOPUS:85057790702
VL - 53
SP - 133
EP - 161
JO - Journal of Complexity
JF - Journal of Complexity
SN - 0885-064X
ER -