On Non-sequential Context Modeling with Application to Executable Data Compression


The sequential context modeling framework is generalized to a non-sequential one by context relaxation from consecutive suffix of the subsequences of symbols to the permutation of the preceding symbols as result of considering complex context structures in such sources as video and program binaries. Context weighting tree is also extended to a series of context trees which are built according to the “model tree”, in which the descendent relationship in the formation of non-sequential context sets is described. Model redundancy and maximum a posteriori model in the framework are discussed and compared. A decision method based on the greedy algorithm is proposed to customize sets of models fitting the concrete sources. Brief description of application to executable data files incorporating with the semantics and syntax constraints are given and experiment are made accordingly as a validation.

Data Compression Conference (dcc 2008)
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.