Treffer: Factorization of natural 4 × 4 patch distributions

Title:
Factorization of natural 4 × 4 patch distributions
Contributors:
UAM. Departamento de Ingeniería Informática, Aprendizaje Automático (ING EPS-001)
Publisher Information:
Springer Berlin Heidelberg
Publication Year:
2015
Collection:
Universidad Autónoma de Madrid (UAM): Biblos-e Archivo
Document Type:
Konferenz conference object
File Description:
application/pdf
Language:
English
Relation:
Lecture Notes in Computer Science; http://dx.doi.org/10.1007/978-3-540-30212-4_15; May 16, 2004; Prague (Czech Republic); Workshop on Statistical Methods in Video Processing, SMVP 2004; http://hdl.handle.net/10486/664646; 165; 174; 3247
DOI:
10.1007/978-3-540-30212-4_15
Rights:
© Springer-Verlag Berlin Heidelberg 2004 ; openAccess
Accession Number:
edsbas.5E804298
Database:
BASE

Weitere Informationen

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-30212-4_15 ; Revised and Selected Papers of ECCV 2004 Workshop SMVP 2004, Prague, Czech Republic, May 16, 2004 ; The lack of sufficient machine readable images makes impossible the direct computation of natural image 4 × 4 block statistics and one has to resort to indirect approximated methods to reduce their domain space. A natural approach to this is to collect statistics over compressed images; if the reconstruction quality is good enough, these statistics will be sufficiently representative. However, a requirement for easier statistics collection is that the method used provides a uniform representation of the compression information across all patches, something for which codebook techniques are well suited. We shall follow this approach here, using a fractal compression–inspired quantization scheme to approximate a given patch B by a triplet (D B , μ B , σ B ) with σ B the patch’s contrast, μ B its brightness and D B a codebook approximation to the mean–variance normalization (B – μ B )/σ B of B. The resulting reduction of the domain space makes feasible the computation of entropy and mutual information estimates that, in turn, suggest a factorization of the approximation of p(B) ≃ p(D B , μ B , σ B ) as p(D B , μ B , σ B ) ≃ p(D B )p(μ)p(σ)Φ(|| ∇ ||), with Φ being a high contrast correction. ; With partial support of Spain’s CICyT, TIC 01–572