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Research on Block Matching Motion Estimation Algorithm in Video Compression

Author: SunMingLi
Tutor: WuYiQuan
School: Nanjing University of Aeronautics and Astronautics
Course: Signal and Information Processing
Keywords: Block Matching Motion Estimation Particle swarm optimization Wavelet -Contourlet transform Independent Component Analysis Artificial Fish School Algorithm Multi - mode search algorithm
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 70
Quote: 1
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With the rapid development of multimedia technology in recent years , many applications are real-time compression of video images put forward higher requirements , fast, and efficient compression algorithm is the key to solving this problem . Motion estimation coding as the core technology of video compression , is also the most complex computing , the most time - consuming part of the motion estimation algorithm for video compression has been a hot research field . And motion estimation algorithm for a comprehensive , in-depth analysis and summary of existing algorithms based on the research and improvement , including the following aspects : First, the particle swarm optimization algorithm is introduced block matching the field of motion estimation , block matching methods of local search and particle swarm algorithm global search based particle swarm optimization algorithm block matching motion estimation algorithm , the experimental results show that the algorithm has good computing accurate degrees and higher time efficiency. Then gives a multi-resolution motion estimation method based on wavelet - Contourlet transform , wavelet -Contourlet domain to be divided according to the intensity of their movement in the low-frequency sub-band image block , to take a different search strategies , and high - frequency sub-band the prediction vector to refine the search . The experimental results show that the method is better than existing multiresolution domain motion estimation algorithm results . Then this paper in a video sequence image frame as the motion component and the static component of the mixed signal , to achieve a method of using the FastICA extracted motion component , and based on the motion component classification matching method for motion estimation . The experimental results indicate that this is a good performance of the block matching method for motion estimation , particularly applicable to small movements and moderate motion image sequence . Finally, we introduce artificial fish swarm algorithm has a good ability of global optimization and local convergence capability , a combination of AFSA and SDSP, while taking advantage of the image sequence space-time correlation of multi-mode fast motion estimation algorithm . Compared with other fast search method , in the occasion of strenuous exercise , the image encoding quality has been improved significantly .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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