Elastic motion estimating method based on improved Levenberg-Marquardt optimization

Abstract

The invention discloses an elastic motion estimating method based on improved Levenberg-Marquardt optimization, and belongs to the field of video coding compression. Aiming at the defect that a Hessian matrix of a reference frame and an inverse matrix thereof need to be recalculated in the process of solving an elastic motion model with the traditional Gauss-Newton algorithm, the Hessian matrix of the reference frame is replaced by that of the current frame with reference to the thought of a Levenberg-Marquardt algorithm, and a hybrid Hessian matrix of the traditional Levenberg-Marquardt optimization algorithm is replaced by a Gauss-Newton Hessian matrix, so that the Hessian matrix and the inverse matrix thereof only need to be calculated once during each motion estimation; meanwhile, an early termination strategy is introduced, so that the purpose of reducing the elastic motion estimation calculation complexity is fulfilled.

Description

technical field [0001] The invention relates to the field of video coding and compression, in particular to an elastic motion estimation method based on improved Levenberg-Mcquard optimization with fast operation speed and high motion compensation quality. Background technique [0002] Since the development of the first generation of video coding standards, although the compression efficiency has been continuously improved, the basic framework of video coding technology has not undergone essential changes so far. Under this framework, motion estimation is an effective temporal dimension difference prediction technique, which is widely used in video coding standards such as H.261 / 2 / 3, MPEG-1 / 2 / 4, H.264 / AVC and AVS. , and most of the improvement in video coding efficiency over the years is due to the use of better motion estimation algorithms. However, the computational resources consumed by the motion estimation process often account for more than 50% of the total resources ...

AI Technical Summary

Problems solved by technology

On the one hand, the existing experimental results show that at the same bit rate, the peak signal-to-noise ratio of the decoded video based on elastic motion estimation is 0.7dB higher than that of block matching motion estimation. If elastic motion estimation is introduced into H.265, the image In the case of about 1% loss of distortion, the elastic motion model can reduce the H.265 bit rate by 3~12%

On the other hand, the Gauss-Newton algorithm of the elastic motion model needs to calculate partial derivatives, Hessian matrix, inverse matrix, matrix multiplication, bilinear interpolation and motion compensation error for each iteration, and its computational complexity is even higher than that of the block translation model The full search has not yet solved the computational efficiency problem of motion estimation

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