Abstract: An image processing device including a decoding section configured to decode an image from an encoded stream, a horizontal filtering section configured to apply a deblocking filter to a vertical block boundary within an image to be decoded by the decoding section, a vertical filtering section configured to apply a deblocking filter to a horizontal block boundary within an image to be decoded by the decoding section, and a control section configured to cause the horizontal filtering section to filter in parallel a plurality of vertical block boundaries included in a processing unit containing a plurality of coding units and cause the vertical filtering section to filter in parallel a plurality of horizontal block boundaries included in the processing unit.

Abstract: An architecture and process are provided that encodes information into a cellular automata memory structure such that it can be recalled utilizing unique memory anchors (engrams) in a manner that both identifies and relates each piece of information relative to other data points. The automata may be individually programmable with a limited, local ruleset that activates other cellular automata based on prior patterns that were fed into the array. Deep Learning Neural Network (DLNN) systems may be probed to understand what discriminators are being used to classify the data, which is not possible with conventional DLNN techniques.

Abstract: Method and system for processing a request associated with a user from a requesting node to an answering node in a telecommunications network. A repository is associated with the answering node, the repository including a data structure including a plurality of user profiles associated with a plurality of users. In the answering node a user profile of the plurality of user profiles is associated with the user. The method comprising the steps of, assigning a unique user index to each user profile in the data structure, wherein the user index is representative of the location of the user profile within the data structure, communicating at least one user index to the requesting node, incorporating the user index in the request by the requesting node, transmitting the request from the requesting node to the answering node, and retrieving the user profile associated with the user associated with the request by the answering node on the basis of the user index.

Abstract: A mechanism is described for facilitating interpolated minimum-maximum compression/decompression for efficient processing of graphics data at computing devices. A method of embodiments, as described herein, includes detecting a tile having pixels representing graphics contents capable of being processed by a graphics processor of a computing device; computing a minimum color value and a maximum color value of the tile. The method may further include splitting the tile into a plurality of interpolation tiles, where each interpolation tile includes a set of pixels of one or more colors. The method may further include computing a plurality of local minimum color values for the plurality of interpolation tiles, computing, based on the plurality of local minimum values, a plurality of residuals for the plurality of interpolation tiles to reduce spreads from the plurality of interpolation tiles, and compressing the reduced plurality of interpolation tiles based on the plurality of residuals.

Abstract: An approximate enumerative coding method (100, 200) and apparatus (300) employ a cardinality-approximating (C-A) lower bound in mapping a message M to a 2-dimensional (2-D) codeword array that satisfies a 2-D constraint. The method (100) includes encoding the message M as a codeword array X using an encoder apparatus. The encoding determines entries in a codeword array X using the C-A lower bound. The C-A lower bound is a function of several terms, namely a memory term k, a cardinality of a set of sequences satisfying a horizontal constraint, a columnar extension probability of the 2-D constraint, and a non-negative constant that is a function of the columnar extension probability. The apparatus (300) includes an encoder processor (310), memory (320) and a computer program (330) stored in the memory (320) and executed by the encoder processor (310).