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: 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 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: 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: 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 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: 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 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.