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 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 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 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 and apparatus for nonlinear multiple motion model and moving boundary extraction are disclosed. In one embodiment, an input image is received, the input image is partitioned into regions/blocks, and the new multiple motion model is applied to each region to extract the motions and associated moving boundaries.

Abstract: A plurality of initial decimation filters are concatenated into an initial combined decimation filter. The initial decimation filters are assigned to a plurality of classes which associate the initial decimation filters with regions of an image to be predicted. An input signal is generated from the initial combined decimation filter and is weighted with a plurality of prediction filters. A correlation matrix and an observation vector are generated for the initial combined decimation filter from the input signal. An optimized combined decimation filter is extracted from the correlation matrix and observation vector. The optimized filter comprises a plurality of optimized decimation filters. The optimized combined decimation filter is de-concatenated into the plurality of optimized decimation filters.

Abstract: An initial value is assigned to a center point for each cluster in a plurality of clusters. Each point in a point space is assigned to a closest cluster based on the distance between the each point and the center of nearest cluster. A first-assignment value is determined for each center point using the clusters the points are assigned to. A first-assignment dynamic validity index of a current cluster configuration is evaluated. Each point in the point space is reassigned to the closest cluster based on the first-assignment value of each center. A second-assignment value is determined for the center of each cluster according to the reassigning. A second-assignment dynamic validity index is evaluated using the second-assignment values. The current cluster configuration is selected if the difference between the dynamic validity indices is less than a threshold.

Abstract: Multi-field taps are defined based on a decimated field. The multi-field taps are used to generate correlation matrices, the elements of which are used to generate covariance matrices. A principal component space is obtained by projecting the correlation matrix elements on to eigenvectors. The principal component space is partitioned into classes and a least square filter set is generated for each class.

Abstract: An initial decimation filter is applied to an original frame to generate a decimated frame. An optimized prediction filter is extracted from both the decimated frame and the original frame, while the initial decimation filter is held fixed. A predicted from is generated from the optimized prediction filter and the decimated frame, and an optimize decimation filter is extracted from the decimated frame and the predicted frame, while the optimized prediction filter remains fixed.

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 sample cell corresponding to a sample is computed to include pixels of an image according to sample cell criteria and shape. The sample cell shape and criteria define pixels to include in the sample cell. A target pixel cell corresponding to a target pixel includes pixels of the image according to a target pixel cell criteria defining which pixels are included in the target pixel cell. A sample filter coefficient is computed from pixels in the sample and target pixel cells. A filter tap corresponding to the target pixel is defined based on the sample filter coefficient. A tap extension corresponding to the target pixel is generated. The tap extension defines additional pixels of the image to be included in an extended target pixel cell according to a tap extension criteria. The extended target pixel cell includes pixel in the target pixel cell and the additional pixels. A tap extension filter coefficient based on pixels located in the extended target pixel cell and the sample cell is computed.

Abstract: An adaptive filter is applied to samples in an initial pattern of samples. The samples in the initial pattern correspond to a subset of the image pixels which are to be predicted using the samples. A level value of each sample in the pattern is varied. The level value corresponds to the intensity/signal value of the sample, and may have a strong effect on the prediction power of the samples. A first prediction pattern (i.e., the prediction signal corresponding to the sampling pattern) is generated from the samples. A sample movement process is performed on a current sample in the first prediction pattern to change a current position of the current sample in the first prediction pattern. A local prediction of pixels influenced by the sample is updated if the sample's position is changed. A sample removal process is performed on the current sample to remove the sample from the first pattern if a criteria is satisfied. A local prediction of pixels is updated if the current sample is removed.

Abstract: A method for performing a sample level variation on an irregular sample is described herein. An initial sample level value for the sample is determined. An area of influence of the sample including pixels for which the sample is a factor in the prediction thereof is computed. Prediction errors of pixels in the area of influence are computed, along with a prediction error of the sample based on the prediction errors of the pixels. If the prediction error of the sample exceeds a prediction error threshold, a sample level change for the sample is computed based on only the prediction errors of the pixels. If the sample level change is less than a level change threshold, a new sample level value is assigned to the sample based on the sample level change.

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 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 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 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 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.