Block Matching with Particle Swarm Optimization for Motion Estimation

Sorkunlu, Niyazi; Şahin, Uğur; Sahin, Ferat

doi:10.1109/smc.2013.226

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…As ME uses the SAD function, the firefly with the minimum SAD should have maximum intensity. Additionally, the number of iterations is controllable and can be adjusted to provide comparable results to TZ search while saving considerable time [10][11][12].…”

Section: Firefly Algorithmmentioning

confidence: 99%

Motion Estimation in HEVC/H.265: Metaheuristic Approach to Improve the Efficiency

Hassan

Butt

2021

The 1st International Conference on Energy, Power and Environment

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An ever increasing use of digital video applications such as video telephony, broadcast and the storage of high and ultra-high definition videos has steered the development of video coding standards. The state of the art video coding standard is High Efficiency Video Coding (HEVC) or otherwise known as H.265. It promises to be 50 percent more efficient than the previous video coding standard H.264. Ultimately, H.265 provides significant improvement in compression at the expense of computational complexity. HEVC encoder is very complex and 50 percent of the encoding consists of Motion Estimation (ME). It uses a Test Zone (TZ) fast search algorithm for its motion estimation, which compares a block of pixels with a few selected blocks in the search region of a referenced frame. However, the encoding time is not suitable to meet the needs of real time video applications. So, there is a requirement to improve the search algorithm and to provide comparable results to TZ search to save a substantial amount of time. In our paper, we aim to study the effects of a meta-heuristic algorithm on motion estimation. One such suitable algorithm for this task is the Firefly Algorithm (FA). FA is inspired by the social behavior of fireflies and is generally used to solve optimization problems. Our results show that implementing FA for ME saves a considerable amount of time with a comparable encoding efficiency.

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Section: Firefly Algorithmmentioning

confidence: 99%

Motion Estimation in HEVC/H.265: Metaheuristic Approach to Improve the Efficiency

Hassan

Butt

2021

The 1st International Conference on Energy, Power and Environment

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“…Another approach would be the combination with other intelligent optimization algorithms, such as the genetic algorithm (GA), and the simulated annealing algorithm (Sharma and Singhal 2015;Nancharaiah and Mohan 2013). Most related research Guo et al (2014), Zhu et al (2014), Sorkunlu et al (2013), Shi and Eberhart (1998), Elbedwehy et al (2012) about the improvement in PSO now mainly focuses on the continuous optimization problems, while the combinatorial optimization problems (e.g., the combination of integer programming and the 0/1 knapsack) do not attract enough attentions, and the current research results are usually suitable to certain scenarios, which are not pervasive.…”

Section: Introductionmentioning

confidence: 99%

CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems

Rong

Trovati

et al. 2016

Soft Comput

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Combinatorial optimization problems are typically NP-hard, due to their intrinsic complexity. In this paper, we propose a novel chaotic particle swarm optimization algorithm (CS-PSO), which combines the chaos search method with the particle swarm optimization algorithm (PSO) for solving combinatorial optimization problems. In particular, in the initialization phase, the priori knowledge of the combination optimization problem is used to optimize the initial particles. According to the properties of the combination optimization problem, suitable classification algorithms are implemented to group similar items into categories, thus reducing the number of combinations. This enables a more efficient enumeration of all combination schemes and optimize the overall approach. On the other hand, in the chaos perturbing phase, a brand-new set of rules is presented to perturb the velocities and positions of particles to satisfy the ideal global search capability and adaptability, effectively avoiding the premature convergence problem found Communicated by V. Loia.

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“…Image Sensing compression is presented on the basis of sparse representation as a kind of theory [1][2] , which is on the image is sparse or compressible, breaking the limitation of the Nyquist specific reason, at the same time of sampling can also be used to compress the image of the new theory, so there is an extremely important research value. Currently, there are many commonly algorithms, such as matching pursuit algorithm (MP) [3] , particle swarm optimization (PSO) Algorithm [4] , orthogonal matching pursuit algorithm [5] and so on.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

Liu²,

Zhang

et al. 2014

AMM

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The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

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Block Matching with Particle Swarm Optimization for Motion Estimation

Cited by 5 publications

References 12 publications

Motion Estimation in HEVC/H.265: Metaheuristic Approach to Improve the Efficiency

Motion Estimation in HEVC/H.265: Metaheuristic Approach to Improve the Efficiency

CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

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