• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.628-635
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• 2018

In this paper, we propose a fast motion estimation algorithm which is important in video encoding. So many fast motion estimation algorithms have been published for improving prediction quality and computational reduction. In the paper, we propose an algorithm that reduces unnecessary computation, while almost keeping prediction quality compared with the full search algorithm. The proposed algorithm calculates the sum of partial block matching error for each candidate, selects the candidates for the next step, compares the stability of optimal candidates with minimum error, and finds optimal motion vectors by determining the progress of the next step. By doing that, we can find the minimum error point as soon as possible and obtain fast computational speed by reducing unnecessary computations. Additionally, the proposed algorithm can be used with conventional fast motion estimation algorithms and prove it in the experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7A
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• pp.828-835
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• 2004

An adaptive hexagon based search(AHBS) algorithm is proposed in this paper to perform block motion estimation in video coding. The AHBS evaluates the value of a given objective function starting from a diamond-shaped checking block and then continues its process using two hexagon-shaped checking blocks until the minimum value is found at the center of checking blocks. Also, the determination of which checking block is used depends on the position of minimum value occurred in previous searching step. The AHBS is compared with other fast searching algorithms including full search(FS). Experimental results show that the proposed algorithm provides competitive performance with slightly reduced computational complexity.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.434-445
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• 2004

Motion Estimation is an essential module in video encoders based on international standards such as H.263 and MPEG. Many fast motion estimation algorithms have been proposed in order to reduce the computational complexity of a well-known full search algorithms(FS). However, these fast algorithms can not work efficiently in DSP processors recently developed for video processing. To solve for this. we propose an efficient motion estimation scheme optimized in the DSP processor like Philips TM1300. A motion vector predictor is pre-estimated and a small search range is chosen in the proposed scheme using strong motion vector correlation between a current macro block (MB) and its neighboring MB's to reduce computation time. An MPEG-4 SP@L3(Simple Profile at Level 3) encoding system is implemented in Philips TM1300 to verify the effectiveness of the proposed method. In that processor, we can achieve better performance using our method than other conventional ones while keeping visual quality as good as that of the FS.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.199-205
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• 2018

So many fast motion estimation algorithms for prediction quality and computational reduction have been published due to tremendous computations of full search algorithm. In the paper, we suggest an algorithm that reduces computation effectively, while keeping prediction quality as almost same as that of the full search. The proposed algorithm based on multiple threshold filter calculates the sum of partial block matching error for each candidate, selects the candidates for the next step, compares the stability of optimal candidates with minimum error, removes impossible candidates, and calculates optimal motion vectors by determining the progress of the next step. By doing that, we can find the minimum error point as soon as possible and obtain the better performance of calculation speed by reducing unnecessary computations. The proposed algorithm can be combined with conventional fast motion estimation algorithms as well as by itself, further reduce computation while keeping the prediction quality as almost same as the algorithms, and prove it in the experimental results.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.959-968
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• 1999

several papers have reported that parallel heuristics or hybrid approaches combining several heuristics can get better results. However, the parallelization and hybridization of any search methods on the single CPU type computer need enormous computation time. that case, we need more elegant combination method. For this purpose, we propose Fast Model of Parallel Heuristics(FMPH). FMPH is based on the island model of parallel genetic algorithms and takes local search to the elite solution obtained form each island(sub group). In this paper we introduce how can we adapt FMPH to the job-shop scheduling problem notorious as the most difficult NP-hard problem and report the excellent results of several famous benchmark problems.

• Journal of Broadcast Engineering
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• v.5 no.2
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• pp.247-259
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• 2000

This paper introduces a new technique that reduces the search times and Improves the accuracy of motion estimation using high temporal and spatial correlation of motion vector. Instead of using the fixed first search Point of previously proposed search algorithms, the proposed method finds more accurate first search point as to compensating searching area using high temporal and spatial correlation of motion vector. Therefore, the main idea of proposed method is to find first search point to improve the performance of motion estimation and reduce the search times. The proposed method utilizes the direction of the same coordinate block of the previous frame compared with a block of the current frame to use temporal correlation and the direction of the adjacent blocks of the current frame to use spatial correlation. Based on these directions, we compute the first search point. We search the motion vector in the middle of computed first search point with two fixed search patterns. Using that idea, an efficient adaptive predicted direction search algorithm (APDSA) for block matching motion estimation is proposed. In the experimental results show that the PSNR values are improved up to the 3.6dB as depend on the Image sequences and advanced about 1.7dB on an average. The results of the comparison show that the performance of the proposed APDSA algorithm is better than those of other fast search algorithms whether the image sequence contains fast or slow motion, and is similar to the performance of the FS (Full Search) algorithm. Simulation results also show that the performance of the APDSA scheme gives better subjective picture quality than the other fast search algorithms and is closer to that of the FS algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.712-719
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• 2007

In this paper, we propose a new, effective, fast motion estimation algorithms using $4{\times}4$ pixels Hadamard transform. The Hadamard transform has the advantage of simplicity because it uses only addition and subtraction. Motion estimation is composed of three stages. First, it should be decided whether to terminate the search early and use a previous motion vector with DC(Direct Current) coefficients. Then the adaptive matching scan order for motion estimation should be determined according to the image complexity using AC(Alternating Current) coefficients. Experimentally, we adapted this algorithms to MVFAST and PMVFAST algorithms, and the proposed algorithms turn out to be very efficient in terms of computational speed while remaining almost the same in terms of PSNR(Peak Signal-to-Noise Ratio) compared to MVFAST and PMVFAST algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.126-131
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• 2004

Full search algorithm(FA) provides the best performance but this is usually impractical because of the large number of computations required for large search region. Fast search and conventional Boolean matching algorithms reduce computational complexity and data processing time but this algorithms have disadvantages that is difficult of implementation of hardware because of high control overhead and that is less performance than FA. This paper presents new Boolean matching algorithm, called BCBM(Bit Converted Boolean Matching). Proposed algorithm has performance closed to the FA by Boolean only block matching that may be very efficiently implemented in hardware for real time video communication. Simulation results show that the PSNR of the proposed algorithm is about 0.08㏈ loss than FA but is about 0.96∼2.02㏈ gain than fast search algorithm and conventional Boolean matching algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.217-220
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• 1996

This paper describes a new hierarchial block matching algorithm especially appropriate for a large search area. The proposed algorithm consists of higher level search for an initial motion vector estimate by using a new matching criterion over the evenly subsampled search points, and lower level search for the final motion vector refinement. In the higher level matching criterion, mean absolute differences at the search points (or motion vector candidates) similar to motion vectors of causally neighboring blocks, are weighted properly so that these points can have a higher chance to being selected. The proposed algorithm outperforms existing hierarchical block matching algorithms, and its computational regularity makes hardware implementation simple.

• International Journal of Fluid Machinery and Systems
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• v.8 no.3
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• pp.209-219
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• 2015

A robust multi-fidelity optimization methodology has been developed, focusing on efficiently handling industrial runner design of hydraulic Francis turbines. The computational task is split between low- and high-fidelity phases in order to properly balance the CFD cost and required accuracy in different design stages. In the low-fidelity phase, a physics-based surrogate optimization loop manages a large number of iterative optimization evaluations. Two derivative-free optimization methods use an inviscid flow solver as a physics-based surrogate to obtain the main characteristics of a good design in a relatively fast iterative process. The case study of a runner design for a low-head Francis turbine indicates advantages of integrating two derivative-free optimization algorithms with different local- and global search capabilities.