Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation
Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation
Standard Codecs: Image Compression to Advanced Video Coding
Standard Codecs: Image Compression to Advanced Video Coding
IEEE Transactions on Circuits and Systems for Video Technology
Novel directional gradient descent searches for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Recursive Dynamically Variable Step Search Motion Estimation Algorithm for High Definition Video
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
An efficient three-step search algorithm for block motion estimation
IEEE Transactions on Multimedia
IEEE Transactions on Consumer Electronics
A fast multi-reference frame motion estimation algorithm
IEEE Transactions on Consumer Electronics
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
A Flexible Heterogeneous Hardware/Software Solution for Real-Time HD H.264 Motion Estimation
IEEE Transactions on Circuits and Systems for Video Technology
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Motion estimation (ME) is the most important component of current video encoders, however, it presents a very high computational complexity. To deal with this complexity, fast ME search algorithms are widely used, since they can greatly speed up this process. Fast search algorithms are vulnerable to choose local minima, producing quality losses, and these losses are more significant when high-definition videos are considered. This work presents a new fast search algorithm for motion estimation, focusing on high-definition videos, named Iterative Random Search (IRS). The IRS algorithm randomly chooses candidate blocks from the reference frame, and, for the best candidates, an iterative refinement is done. The central position is also evaluated through an iterative process. By using this combination of strategies, the IRS becomes less susceptible to local minima falls. Achieved results show that, for 1080p sequences, IRS generates the highest quality results when compared to well known fast algorithms, such as Diamond Search, Four Step Search and Three Step Search. The quality gains can be higher than 4 dB, while the number of evaluated candidate blocks may increase, at most, 2.6 times. Additionally, the average quality loss against Full Search is 1.45 dB, while the number of evaluated candidate blocks can achieve a reduction higher than 200 times.