Motion estimation techniques for video coding

Pao Yue-kong Library Electronic Theses Database

Motion estimation techniques for video coding

 

Author: Chu, Carlson
Title: Motion estimation techniques for video coding
Degree: M.Sc.
Year: 1995
Subject: Image processing -- Digital techniques
Coding theory
Motion perception (Vision)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: 1 v. (various pagings) : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1183438
URI: http://theses.lib.polyu.edu.hk/handle/200/1820
Abstract: Motion compensation is extensively used in low bit-rate coding because of its abilities to exploit temporal correlation between successive frames of an image sequence. Motion estimation which analyzes the movement of objects in an image sequence is an important element of video coding systems. To estimate the displacement vector of a moving object from two successive frames, a number of techniques have been developed. The block matching (BM) approach performs well in block-based coding schemes, such as those using discrete cosine transform (DCT) and has been adopted in existing video coding standards like H.261 and MPEG-1. The full-search block matching algorithm has widely been used due to its simplicity and ease of implementation in hardware. However, the computational complexity of the full-search algorithm is still too high for real-time implementation, In the last decade, many research works have been carried out to find efficient algorithms for block matching motion estimation. An adaptive full-search blocking matching algorithm is proposed for motion estimation in this dissertation. In the proposed method, the search starting point as well as the search range can be adapted to local statistics of input blocks. A new criterion called improved pel difference classification (IPDC) is also employed. Simulation results show that the proposed algorithm achieves better MSE performance for motion-prediction images and requires about 6 times less number of computations than traditional full-search method.

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