%0 Journal Article
%T Full Image Inference Conditionally upon Available Pieces Transmitted into Limited Resources Context
%A Rodrigue Saoungoumi-Sourpele
%A Jean Michel Nlong
%A David Jaur¨¨s Fotsa-Mbogne
%A Jean-Robert Kala Kamdjoug
%A Laurent Bitjoka
%J Journal of Signal and Information Processing
%P 57-69
%@ 2159-4481
%D 2021
%I Scientific Research Publishing
%R 10.4236/jsip.2021.123003
%X In a context marked by the proliferation of
smartphones and multimedia applications, the processing and transmission of
images have become a real problem. Image compression is the
first approach to address this problem, it nevertheless suffers from its
inability to adapt to the dynamics of limited environments, consisting mainly
of mobile equipment and wireless networks. In this work, we propose a
stochastic model to gradually estimate an image upon information on its pixels that are transmitted progressively. We
consider this transmission as a dynamical process, where the sender pushes the data in decreasing significance order. In order to adapt to network
conditions and performances, instead of truncating the pixels, we suggest a new
method called Fast Reconstruction Method by Kalman Filtering (FRM-KF)
consisting of recursive inference of the not yet received layers belonging to a
sequence of bitplanes. After empirical analysis, we estimate parameters of our model which is a linear
discrete Kalman Filter. We assume the initial law of information to be the
uniform distribution on the set [0, 255] corresponding to the range of gray
levels. The performances of FRM-KF method
%K Progressive Image Transmission
%K Bitplane Coding
%K Kalman Filtering
%K Fast Reconstruction
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=110348