Motion estimation method

Abstract

The invention discloses a motion estimation method, wherein motion vectors of adjacent macroblock in reference frame are calculated to be a prior probability of prediction motion vector of current macroblock, the motion vectors of adjacent macroblock in the current frame is obtained to be a conditional probability of the prediction motion vector of the current macroblock, according to the prior probability and the conditional probability, the motion vectors of the adjacent macroblock are calculated in the current frame to be a posterior probability of the prediction motion vector of the current macroblock, and the motion vectors of the adjacent macroblock with the biggest posterior probability is chosen to be the prediction motion vector of the current macroblock. Accordingly, the Bayesian method is used to predict the prediction motion vector of the current frame, so that the prediction is more accurate and simple, the amount of calculation is reduced, and the accuracy of the prediction motion vector is ensured.

Description

【Technical field】 [0001] The invention relates to the field of image processing, in particular to a motion estimation method. 【Background technique】 [0002] In video compression coding, video is an image sequence formed by continuous image frames. Due to the limitation of scene change speed, there is a high correlation between adjacent frames, that is, there is high temporal and spatial redundancy. Motion estimation is an important method to use the temporal correlation between adjacent frames in the video image sequence to establish the expressive relationship between adjacent frames, thereby reducing time redundancy and improving the efficiency of video compression and coding. Motion estimation is a key technology in video compression. In a video compression scheme, motion estimation usually accounts for 60%-80% of the total calculation. The result directly affects the coding efficiency of images and the quality of image restoration. [0003] Existing motion estimation m...

AI Technical Summary

Problems solved by technology

[0004] The shortcomings of the existing technology: (1) The block motion estimation algorithm using a fixed search mode believes that the motion estimation matching error decreases monotonically as the search direction moves towards the global minimum error value position, and the error surface is unimodal, so no matter how severe Both moving images and relatively still images are treated equally, which will inevitably lead to computational redundancy

(2) The hierarchical block motion estimation algorithm assumes that the motion vector (Motion Vector, referred to as MV) obtained by the large macroblock can predict the smaller block well, but in fact it often leads to the wrong matching direction, and the effect is not good.

(3) The hybrid search algorithm has high computational complexity, and its complex model requires more bits to transmit when decoding

(4) The adaptive cross-pattern search algorithm only uses the spatial correlation of motion vectors

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