Video coding method and device

Abstract

The invention relates to a method and device for performing the movement forecast in the video coding, the method comprises a. selecting a set of reference areas from the coded areas on the current frame; b. estimating the movement vector difference values between the current area and each reference area; c. determining the reference area corresponding to the minimum movement vector difference value, and judging that whether the minimum movement vector difference value is smaller than a preset threshold value; d. setting the movement vector and the reference frame of the current area as the determined movement vector of the reference area and the reference frame, if the minimum movement vector difference value is smaller than the preset threshold value; e. if the minimum movement vector difference value is larger than the preset threshold value, for each forecast direction, setting the movement vector of the current area as 0 vector or an intermediate vector, and setting the reference frame of the current area as the reference frame most adjacent to the current area or the reference frame most constantly adopted by one set of reference areas. According to the invention, the high coding performance can be obtained.

Description

Technical field [0001] The present invention generally relates to digital video processing, and particularly relates to a video encoding method and device. Background technique [0002] In modern video coding standards such as MPEG-1, MPEG-2, MPEG-4, H.261, H.263, H.264 and AVS, Motion Estimation (ME) and Motion Compensation (MC) are important tools. Used to suppress temporal redundancy, so as to obtain high coding efficiency. In ME / MC, the current frame is divided into non-overlapping blocks. For each current block in a current frame, a search area is defined in the reference frame. Each point in the search area corresponds to a candidate block, which may be a good prediction of the current block. A similarity metric is also defined to search in the search area and find candidate blocks that have great similarity (if not the maximum similarity) to the current block according to the similarity metric. These candidate blocks with great similarity will be the best matching bloc...

AI Technical Summary

Problems solved by technology

However, this process is also inefficient, as it derives the motion vector for the current block from the block position in the reference frame (where the reference frame is selected in a block matching process), rather than from the coded region in the current frame The block's motion vector is derived from the

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