Earticle

초록

영어

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the mean square error (MSE). Unfortunately, the MSE evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. Recently, several fast BM algorithms have been proposed to reduce the number of MSE operations by calculating only a fixed subset of search locations at the price of poor accuracy. The parallel cat swarm optimization (PCSO) & enhanced parallel cat swarm optimization (EPCSO) methods are an optimization algorithms designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. In this paper, a new algorithm based on Hybrid Cat Swarm Optimization (HCSO) is proposed to reduce the number of search locations in the BM process. In proposed algorithm, the computation of search locations is drastically reduced by adopting a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational time and find the optimal solutions in a very short time.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Cat Swarm Optimization (CSO)
  3.1. Seeking Mode: Resting and Observing
  3.2 Tracing Mode: Running After a Target
 4. CSO Movement = Seeking Mode + Tracing Mode
 5. Parallel Cat Swarm Optimization (PCSO)
  5.1. Parallel Tracing Mode Process
  5.2. Information Exchanging Process
 6. Average-Inertia Weighted Cat Swarm Optimization (AICSO)
 7. Fitness Approximation Method
  7.1. Updating the Individual Database
  7.2. Fitness Calculation Strategy
 8. Proposed Algorithm
 9. Simulation Results
 10. Conclusion
 References

키워드

저자

• Israa Hadi [ Professor College of Information Technology University of Babylon ]
• Mustafa Sabah [ Ph.D. Student, College of Information Technology University of Babylon ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605