Abstract
ABSTRACT In this paper, we propose two new fast algorithms for motion vector (MV) estimation using spatial correlationof MVs in adjacent blocks. We select a set of MV candidates based on the MV knowledge of its neighboringblocks, and then perform further search to refine the MV result. The first algorithm is performed on each blockconsecutively. The second algorithm is a modified version of the first one by using a block subsampling techniqueto reduce computational cost. We show with experimental results that, compared with full search block matchingalgorithm (FBMA), the proposed algorithms have a speed-up factor ranging from 50 to 100 with only 2-15% MSEincrease when applied to typical test image sequences.Keywords: motion vector estimation, spatial correlation, block matching algorithm, video coding 1 INTRODUCTION Video image compression has been an active research area recently, since it plays an essential role in trans-mission and storage of digital video data. The applications includes multimedia transmission, digital TV, tele-conferencing, videophone, CD-ROM storage, etc. The main idea of video compression is to exploit both spatialand temporal redundancies inherent in image sequences. A commomly used method for reducing temporal re-dundancy is motion compensated predictive coding, which is also employed in the MPEG standard [1], [8], [9].One key ingredient in motion compensated coding is motion vector (MV) estimation.The block matching technique has been widely used for MV estimation due to its simplicity. The full searchblock matching algorithm (FBMA) performs search at all locations in a fixed search window and provides an
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