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.