• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.539-547
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• 2003

According to the PDE scheme, it is desirable to make the value of SAD to be rapidly increased in motion estimation. In this paper, we propose a new block matching algorithm based on the combination of a new scan and search order. In the proposed scan order, a current block is divided by the frequency that is estimated by summing the difference between the mean and the values of the pixels. And then the calculation of SAD is performed from the highest frequency area. In the proposed search order based on DC difference between the current and the candidate block, candidate blocks with low DC are early checked in motion estimation. Simulation results show that the proposed method achieves about 6% computational gain compared with the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1401-1410
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• 1999

In this paper, we propose a fast block matching algorithm using the lower and upper bound of mean absolute difference (MAD) which is calculated at the search region overlapped with neighbor blocks. At first, we calculate the lower bound of MAD and reduce the search point by using this lower bound. In this method, we can get good prediction error performance close to full search block matching algorithm (FSBMA), but there exists some computational complexity that has to be reduced. Therefore, we further reduce the computational complexity by using pixel subsampling besides the lower and upper bound of MAD. Experimental results show that we can remarkably reduce the computational complexity with good prediction error performance close to FSBMA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.9-15
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• 2007

We propose the fast full search algorithm that reduces the computation of the block matching algorithm which is used for motion estimation of the video coding. Since the conventional spiral search method starts searching at the center of the search window and then moves search point to estimate the motion vector pixel by pixel, it is good for the slow motion pictures. However the proposed method is good for the fast and slow motion because it estimates the motion in the new search order after partitioning the search window. Also, when finding the motion vector, this paper presents the method that reduces the complexity by computing the matching error in the order which is determined by local image complexity. The proposed algorithm reduces the computation up to 99% for block matching error compared with the conventional spiral full search algorithm without any loss of image quality.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.55-60
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• 2020

In this paper, we propose a fast block matching algorithm of motion estimation using early detection of optimal candidate with high priority and a threshold. Even though so many fast algorithms for motion estimation have been published to reduce computational reduction full search algorithm, still so many works to improve performance of motion estimation are being reported. The proposed algorithm calculates block matching error for each candidate with high priority from previous partial matching error. The proposed algorithm can be applied additionally to most of conventional fast block matching algorithms for more speed up. By doing that, we can find the minimum error point early and get speed up by reducing unnecessary computations of impossible candidates. The proposed algorithm uses smaller computation than conventional fast full search algorithms with the same prediction quality as the full search algorithm. Experimental results shows that the proposed algorithm reduces 30~70% compared with the computation of the PDE and full search algorithms without any degradation of prediction quality and further reduces it with other fast lossy algorithms.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1990.10a
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• pp.289-293
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• 1990

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.247-256
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• 2004

In this paper, we propose the prediction search algorithm for block matching using the temporal/spatial correlation of the video sequence and the renter-biased property of motion vectors The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(Sum of Absolute Difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate pint in each search region and the predicted motion vector from the neighbour blocks of the current frame. And the searching process after moving the starting point is processed a adaptive search pattern according to the magnitude of motion vector Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 0.75dB as depend on the video sequences and improved about 0.05∼0.34dB on an average except the FS (Full Search) algorithm.

• Journal of Broadcast Engineering
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• v.12 no.5
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• pp.503-515
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• 2007

A new fast motion estimation algorithm is presented in this paper. The algorithm, named Enhanced Cross-Diamond Search (ECDS), is based on the Diamond Search (DS) algorithm. The DS algorithm, even though faster than the most well-known algorithms, was found not to be very robust in terms of objective and subjective qualities for several sequences and the algorithm searches unnecessary candidate blocks. We propose a novel ECDS algorithm using a small cross search as the initial step, and large/small DS patterns as subsequent steps for fast block motion estimation. Experimental results show that the ECDS is much more robust, provides a faster searching speed, and smaller distortions than other popular fast block-matching algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.41-56
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• 2000

In this paper, we propose a two-stage fast block matching algorithm using the mean absolute error (MAE) of neighbor search point that can reduce the computational complexity to estimate motion vector while the motion estimation error performance is nearly the same as full search algorithm (FSA) In the proposed method, the lower bound of MAE 6at current search point IS calculated using the MAE of neighbor search point And we reduce the computational complexity by performing the block matching process only at the search point that has to be block matched using the lower bound of MAE The proposed algorithm is composed of two stages The experimental results show that the proposed method drastically reduces the computational complexity while the motion compensated error performance is nearly kept same as that of FSA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1C
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• pp.19-33
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• 2008

A method that is adaptively selecting among previous fast motion estimation algorithms and a newly proposed fast motion estimation algorithm(UCDS) is presented in this paper. The algorithm named AUDC and a newly proposed fast motion estimation algorithms are based on the Diamond Search(DS) algorithm and Three Step Search(TSS). Although many previous fast motion estimation algorithms have lots of advantages, those have lots of disadvantages. So we thought better adaptive selection of fast motion estimation algorithms than only using one fast motion estimation algorithm. Therefore, we propose AUDC that is using length of the MV, Search Point, SAD of the neighboring block and adaptively selecting among Cross Three Step Search(CTSS), Diamond Search(DS) and Ungraded Cross Diamond Search(UCDS). Experimental results show that the AUDC is much more robust, provides a faster searching speed, and smaller distortions than other popular fast block-matching at algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.338-343
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• 2004

In Block Matching Algorithm (BMA), a search pattern has a very important affect on the search time and the output quality. In this paper, we propose the HDS( Half Diamond Search) pattern based on the cross center-biased distribution property of a motion vector. At lust, the 4 points in the above, below, left, and right around the search center is calculated to decide the point of the MBD (Minimum Block Distortion). And an above point of the MBD is checked to calculate the SAD. If the SAD is less than the previous MBD, this process is repeated. Otherwise, the left and right points of MBD are calculated to decide the points that have the MBD between two points. These processes are repeated to the predicted direction for motion estimation. Experiments show that the speedup improvement of the proposed algorithm can be up to 23% while maintaining similar image quality.