Rate distortion optimization (RDO) technique is the best known mode decision method in recent video coding standard, such as H.264 and AVS. However, the unbearable computational burden limits its application. According to the proposed block-level pipeline architecture of RDO-based MD, we find that zigzag scanning and entropy coding are the bottlenecks. In our paper, we firstly analyze the time ...

Constraint Satisfaction Problems typically exhibit strong combinatorial explosion. In this paper we present some models and techniques aimed at improving efficiency in Constraint Logic Programming. A hypergraph model of constraints is presented and an outline of strategy planning approach focused on entropy minimization is put forward. An example cryptoaritmetic problem is explored in order to ...

In the past ten years, the areas of probabilistic inductive logic programming and statistical relational learning put forth a large collection of approaches to combine relational representations of knowledge with probabilistic reasoning. Here, we develop a series of evaluation and comparison criteria for those approaches and focus on the point of view of knowledge representation and reasoning. ...

1. Introduction Cellular Automata have generated much interest [1,2,3] because of their diverse behavior and usefulness as a discrete model for many processes. Wolfram's 1984 paper on universality and complexity in cellular automata [3,4] described four classes of behavior for automata: class 1 for homogeneous stable behavior, class 2 for simple periodic patterns, class 3 for chaotic aperiodic ...

In many applications, the use of Bayesian probability theory is problematical. Information needed to feasibility calculate is unavailable. There are different method-ologies for dealing with this problem, e.g., maximal entropy and Dempster-Shafer Theory. If one can make independence assumptions, many of the problems disappear, and in fact, this is often the method of choice even when it is obvi...

Works, briefly surveyed here, are concerned with two basic methods: Maximum Probability and Bayesian Maximum Probability; as well as with their asymptotic instances: Relative Entropy Maximization and Maximum Non-parametric Likelihood. Parametric and empirical extensions of the latter methods – Empirical Maximum Maximum Entropy and Empirical Likelihood – are also mentioned. The methods are viewe...

Uncertain logic is a branch of multi-valued logic for dealing with subjective information, which explains formulas as uncertain variables and defines their truth values as uncertain measures. In this paper, uncertain satisfiability is proposed to verify the consistency of a set of truth values for the formulas. Furthermore, for a set of satisfiable formulas with given truth values, the problem ...

Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why uses this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficul...

Image segmentation is one of the key techniques in the field of image understanding and computer vision. To determine the optimal threshold in image segmentation, an effective image threshold segmentation method based on fuzzy logic is presented. A new kind of fuzzy entropy is defined, that is not only related to the membership, but also related to probability distribution. According to the max...

This paper suggests an effective method for facial recognition using fuzzy theory and Shannon entropy. Combination of fuzzy theory and Shannon entropy eliminates the complication of other methods. Shannon entropy calculates the ratio of an element between faces, and fuzzy theory calculates the membership of the entropy with 1. More details will be mentioned in Section 3. The learning performanc...