Abstract: For use in media communications, e.g. in the delivery of video and audio or in teleconferencing, a media file or title is partitioned for secured, efficient delivery. A data sequence representing the file is partitioned into smaller subsequences, with a first subsequence being chosen to convey a degraded-quality representation of the data, and the other sequences for complementing the first subsequence to obtain a superior-quality representation. Among areas of applicability are media on demand, real-time communications and broadcasting, and online media locker, jukebox and chat room services.

Abstract: Temporal classified filtering encodes image data by applying filters assigned to classes of pixels in a target frame to predict values for the pixels. The pixels are classified based on their associated motion vectors and the motion vectors are used to position the filters on the reference frame. Prediction error values are also calculated. The filters, motion vectors, and prediction errors represent the pixels in the encoded image data. The reference frame may be a past or future frame of the image data, and multiple reference frames of various combinations of past and future frames may be used. The filters for multiple reference frames are three-dimensional comprising a two-dimensional filter for each reference frame. The filters may be pre-determined or generated as the frames are encoded. The image data is recreated by applying the filters to the reference frames and correcting the resulting predictions with the prediction error values.

Abstract: A method of irregular motion compensation includes using contours of objects in a reference image to tile the reference image into a plurality of irregular shapes, and mapping each irregular shape to a location in a target image by assigning a motion vector to each irregular shape.

Abstract: A technique for performing motion estimation using an adaptive motion search range includes calculating motion vectors for blocks of pixels in a target image, using the calculated motion vectors to generate a search range associated with a target block of pixels in the target image, and using the generated search range to estimate motion of the target block of pixels.

Abstract: A method of generating an adaptive temporal filter is performed by constructing a motion vector area cell around each of a plurality of motion vectors in a target image, selecting a pixel in the target image, constructing a pixel area cell around the selected pixel, determining an overlap area between the motion vector area cells and the pixel area cell, generating filter weights from the overlap area, and using the filter weights to filter the selected pixel.

Abstract: A method for determining values of motion vectors includes receiving an irregular pattern of motion vectors for a target image, estimating an initial value for each of the motion vectors, using the motion vectors to generate a tap structure for an adaptive temporal prediction filter, and using the tap structure to re-estimate the value of each motion vector.

Abstract: A method of performing adaptive temporal prediction includes receiving a target image, wherein a position of an object in the target image is different from a position of the object in a reference image, using a boundary of the object to generate an irregular pattern of target image motion vectors, using the target image motion vectors to partition the target image into area of influence cells, wherein each area of influence cell contains a unique one of the target image motion vectors, and generating a prediction of the target image by applying an adaptive area of influence filter to the area of influence cells.

Abstract: A method of performing a temporal prediction includes using an irregular filter tap to produce a prediction of a target image, and applying a regular filter tap to the prediction to produce an enhanced prediction.

Abstract: A method of filtering an image includes receiving target image data and applying a multiple stage filter to the target image data. Each stage of the filtering includes generating a motion vector sampling pattern, using the target image data and the motion vector sampling pattern to generate a temporal prediction of the target image data, and using the temporal prediction of the target image data to generate a spatial-temporal transformation of the target image data.

Abstract: A method of creating a variable-shaped block in a target image includes generating an initial pattern of target blocks, identifying allowable combinations of target blocks, calculating a combination value for each allowable combination, using the combination value to select one of the allowable combinations, and performing the selected allowable combination to create a variable-shaped block.

Abstract: A multi-level transform generates descriptions containing compressed data that represents source data using a description generation operation and variable support filters for compaction at each level. The initial level filters the source data and each subsequent level operates on data filtered by a prior level. The description assignment and filtering at each level may vary to create different operating points for the multi-level transform. Each operating point may have a corresponding error recovery process. In one aspect, an error recovery process encodes additional descriptions that are combined with non-erroneous description data to provide error recovery of the data in missing or damaged descriptions. In another aspect, a multi-level transform is created by combining description generation and variable support filters at the various levels.

Abstract: A method of generating a spatial prediction of a target block of pixels in a target image includes generating a spatial vector for a target block of pixels, using the spatial vector to construct a spatial reference block, and using the spatial reference block to generate the spatial prediction of the target block.

Abstract: A method of extracting a moving object boundary includes estimating an initial motion vector for an object whose motion is represented by a change in position between a target image and a reference image, estimating an initial vector for a background area over which the object appears to move, using the estimated vectors to find a first iteration of a dynamical model solution, and completing at least one subsequent iteration of the dynamical model solution so as to extract a boundary of the object.

Abstract: A method of selecting motion vectors includes receiving a set of motion vectors and a target rate, and using a rate-distortion criterion to modify the set of motion vectors.

Abstract: A method of performing adaptive temporal prediction includes receiving a target image, wherein a position of an object in the target image is different from a position of the object in a reference image, using a boundary of the object to generate an irregular pattern of target image motion vectors, using the target image motion vectors to partition the target image into area of influence cells, wherein each area of influence cell contains a unique one of the target image motion vectors, and generating a prediction of the target image by applying an adaptive area of influence filter to the area of influence cells.

Abstract: A method of generating a spatial prediction of a target block of pixels in a target image includes generating a spatial vector for a target block of pixels, using the spatial vector to construct a spatial reference block, and using the spatial reference block to generate the spatial prediction of the target block.

Abstract: A method of performing a temporal prediction includes using an irregular filter tap to produce a prediction of a target image, and applying a regular filter tap to the prediction to produce an enhanced prediction.

Abstract: A technique for performing motion estimation using an adaptive motion search range includes calculating motion vectors for blocks of pixels in a target image, using the calculated motion vectors to generate a search range associated with a target block of pixels in the target image, and using the generated search range to estimate motion of the target block of pixels.

Abstract: A method of generating an adaptive temporal filter is performed by constructing a motion vector area cell around each of a plurality of motion vectors in a target image, selecting a pixel in the target image, constructing a pixel area cell around the selected pixel, determining an overlap area between the motion vector area cells and the pixel area cell, generating filter weights from the overlap area, and using the filter weights to filter the selected pixel.

Abstract: A method of filtering an image includes receiving target image data and applying a multiple stage filter to the target image data. Each stage of the filtering includes generating a motion vector sampling pattern, using the target image data and the motion vector sampling pattern to generate a temporal prediction of the target image data, and using the temporal prediction of the target image data to generate a spatial-temporal transformation of the target image data.