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Adaptive order search and tangent-weighted trade-off for motion estimation in H.264
Author(s) -
Srinivas Bachu,
K. Manjunatha Chari
Publication year - 2016
Publication title -
journal of king saud university - computer and information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 33
eISSN - 2213-1248
pISSN - 1319-1578
DOI - 10.1016/j.jksuci.2016.07.002
Subject(s) - block (permutation group theory) , motion estimation , computer science , algorithm , peak signal to noise ratio , matching (statistics) , rate–distortion optimization , search algorithm , process (computing) , computational complexity theory , block matching algorithm , tangent , mathematical optimization , artificial intelligence , mathematics , pattern recognition (psychology) , image (mathematics) , video processing , video tracking , statistics , geometry , operating system
Motion estimation and compensation play a major role in video compression to reduce the temporal redundancies of the input videos. A variety of block search patterns have been developed for matching the blocks with reduced computational complexity, without affecting the visual quality. In this paper, block motion estimation is achieved through integrating the square as well as the hexagonal search patterns with adaptive order. The proposed algorithm is called, AOSH (Adaptive Order Square Hexagonal Search) algorithm, and it finds the best matching block with a reduced number of search points. The searching function is formulated as a trade-off criterion here. Hence, the tangent-weighted function is newly developed to evaluate the matching point. The proposed AOSH search algorithm and the tangent-weighted trade-off criterion are effectively applied to the block estimation process to enhance the visual quality and the compression performance. The proposed method is validated using three videos namely, football, garden and tennis. The quantitative performance of the proposed method and the existing methods is analysed using the Structural SImilarity Index (SSIM) and the Peak Signal to Noise Ratio (PSNR). The results prove that the proposed method offers good visual quality than the existing methods

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