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 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 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 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 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: In one embodiment, a video encoder encodes digital video using motion segmentation. According to one embodiment, motion segmentation is performed using multiple frames. The motion segmentation may operate by determining multiple classification hypotheses and by re-classifying poorly classified regions according to the multi-frame hypothesis tracking algorithm. This involves determining a similarity measure for each hypothesis class, and then assigning a classification to the region with the hypothesis class that is most similar or consistent with past or future data. This hypothesis tracking algorithm for re-classification can handle inherent problems like occlusion and ambiguous motion classification, that plague standard methods. In another embodiment, the motion segmentation algorithm may be integrated into a larger video encoding system.

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 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 method and apparatus for adaptive pixel estimation under high error rate conditions are disclosed. Classes are created, feasible classes are determined, and an optimal filter is applied to perform the pixel estimation.

Abstract: A method including decimating a signal x using a filter f to obtain a decimated signal y, interpolating the decimated signal y to obtain a reconstructed signal z, determining a refinement factor s by decimating z and comparing decimated z to the decimated signal y, and determining an improved reconstructed signal r by using the refinement factor s, the filter f, and the reconstructed signal z is disclosed.

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 method and apparatus for adaptive pixel estimation under high error rate conditions are disclosed. Classes are created, feasible classes are determined, and an optimal filter is applied to perform the pixel estimation.

Abstract: In one embodiment, a video encoder encodes digital video using motion segmentation. According to one embodiment, motion segmentation is performed using multiple frames. The motion segmentation may operate by determining multiple classification hypotheses and by re-classifying poorly classified regions according to the multi-frame hypothesis tracking algorithm. This involves determining a similarity measure for each hypothesis class, and then assigning a classification to the region with the hypothesis class that is most similar or consistent with past or future data. This hypothesis tracking algorithm for re-classification can handle inherent problems like occlusion and ambiguous motion classification, that plague standard methods. In another embodiment, the motion segmentation algorithm may be integrated into a larger video encoding system.