Least-square prediction for backward adaptive video coding
EURASIP Journal on Applied Signal Processing
A spatio-temporal auto regressive model for frame rate upconversion
IEEE Transactions on Circuits and Systems for Video Technology
New frame rate up-conversion using bi-directional motion estimation
IEEE Transactions on Consumer Electronics
Efficiency analysis of multihypothesis motion-compensated prediction for video coding
IEEE Transactions on Image Processing
Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation
IEEE Transactions on Image Processing
Detection of missing data in image sequences
IEEE Transactions on Image Processing
Interpolation of missing data in image sequences
IEEE Transactions on Image Processing
A method for motion adaptive frame rate up-conversion
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive interpolation filters and high-resolution displacements for video coding
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, a motion-aligned auto-regressive (MAAR) model is proposed for frame rate up conversion, where each pixel is interpolated as the average of the results generated by one forward MAAR (Fw-MAAR) model and one backward MAAR (Bw-MAAR) model. In the Fw-MAAR model, each pixel in the to-be-interpolated frame is generated as a linear weighted summation of the pixels within a motion-aligned square neighborhood in the previous frame. To derive more accurate interpolation weights, the aligned actual pixels in the following frame are also estimated as a linear weighted summation of the newly interpolated pixels in the to-be-interpolated frame by the same weights. Consequently, the backward-aligned actual pixels in the following frame can be estimated as a weighted summation of the corresponding pixels within an enlarged square neighborhood in the previous frame. The Bw-MAAR is performed likewise except that it is operated in the reverse direction. A damping Newton algorithm is then proposed to compute the adaptive interpolation weights for the Fw-MAAR and Bw-MAAR models. Extensive experiments demonstrate that the proposed MAAR model is able to achieve superior performance than the traditional frame interpolation methods such as MCI, OBMC, and AOBMC, and it is even better than STAR model for the most test sequences with moderate or large motions.