Abstract: The present invention comprises a distributed data processing system including a plurality of data processing elements for expeditiously performing an encoding or prediction function pursuant to a context-based model in an adaptive, optimal and time-progressive manner. The distributed data processing system, having access to each symbol of an input data string at each clock cycle, adaptively generates context-relevant data sets which provide the best model for coding or prediction based on the input symbols. Each symbol and its best model for encoding or prediction emerge concurrently from the system, resulting in a favorable time complexity of .omicron.(n) for an n-symbol input data string.

Abstract: A method, system, and manufacture are provided, for use in connection with data processing and compression, for quantizing a string of data values, such as image data pixel values. The quantization is achieved by grouping the data values, based on their values, into a predetermined number of categories, each category containing the same total number of values. For each category, a value, preferably a mean value of those in the category, is selected as a quantization value. All of the data values in the category arc then represented by the selected quantization value. For data strings having a dependency (that is, the values of one or more of the data values provide information about values of other of the data values), the dependency is modeled by a method in which a modeling algorithm defines contexts in terms of a tree structure, and the basic method of grouping into categories and selecting a quantization value for each category is performed on a per node (i.e., per context) basis.

Abstract: This technique for coding images involves filtering the original input image into subband images using successive high-pass and low-pass filterings over a tree-shaped filter with each tree filtering stage involving re-scaling operations adjusted to keep the subband image signal dynamics substantially stable throughout the filtering tree.

Abstract: The present invention comprises a distributed data processing system including a plurality of data processing elements for expeditiously performing an encoding or prediction function pursuant to a context-based model in an adaptive, optimal and time-progressive manner. The distributed data processing system, having access to each symbol of an input data string at each clock cycle, adaptively generates context-relevant data sets which provide the best model for coding or prediction based on the input symbols. Each symbol and its best model for encoding or prediction emerge concurrently from the system, resulting in a favorable time complexity of O(n) for an n-symbol input data string.

Abstract: A device for adaptively encoding a flow of data symbols x.sub.1, x.sub.2, . . . , x.sub.t taken within a predefined symbol family, into a compressed bit string c(s), said method including so-called modelling operations (see Modelling Unit 60) whereby the numbers of times each symbol occurs in the flow are collected in a tree architecture denoting past flow symbols considered in a predefined order according to a reordering function f(t), and Relative Efficiency Counter (REC) facilities embedded in the growing tree nodes, and tracking REC variations for both optimal coding node determination and tree updating operations. The compressed bit string is then generated by an Arithmetic Coder (64) connected to said Modelling Unit (60).