Hardware efficient block matching algorithm based on modified differential evolution optimization for fast motion estimation

Every video coding standard includes and requires motion estimation and compensation. The full search algorithm, which provides the best motion estimation, has a very high computation cost. Researchers have developed several algorithms to reduce the cost of computation. However, most of these algorithms become trapped in local minima during the search. Population-based evolutionary algorithms are widely used to develop a computationally e cient and cost-effective motion estimation strategy. The most recent effort used the Jaya algorithm to develop a motion estimation process that outperformed the state-of-the-art test zone search algorithm. In this study, a motion estimation algorithm based on the ant weight lifting approach is proposed. Previously, the ant weight-lifting algorithm was used to solve a variety of problems such as image segmentation, signal compression, and so on. The ant weight-lifting algorithm's computation cost was reduced by adopting a tness estimation method that uses nearest neighbor interpolation and an early termination strategy. Compared to Jaya algorithm-based motion estimation, the proposed algorithm executes up to 3% more quickly and exhibits up to 1.2 dB less distortion.

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Motion estimation algorithm for images of vital signs monitors using Block Matching

Alvarez-Bossa

Flórez-Ordónez

Zabala-Vargas

et al. 2019

2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA)

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Hardware efficient block matching algorithm based on modified differential evolution optimization for fast motion estimation

Cited by 4 publications

References 26 publications

FPGA Design of Area Efficient and Superfast Motion Estimation Using JAYA Optimization-Based Block Matching Algorithm

FPGA Design of Area Efficient and Superfast Motion Estimation Using JAYA Optimization-Based Block Matching Algorithm

Ant weight-lifting algorithm for motion estimation

Motion estimation algorithm for images of vital signs monitors using Block Matching

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