• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.81-95
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• 1996

In this paper, we propose a motio estiamtion algorithm using low-resolution quantization to reduce the computation of the full search algorithm. The proposed algorithm consists of the low-resolution search which determins the candidate motion vectors by comparing the low-resolution image and the full-resolution search which determines the motion vector by comparing the full-resolution image on the positions of the candidate motion vectors. The low-resolution image is generated by subtracting each pixel value in the reference block or the search window by the mean of the reference block, and by quantizing it is 2-bit resolution. The candidate motion vectors are determined by counting the number of pixels in the reference block whose quantized codes are unmatched to those in the search window. Simulation results show that the required computational cost of the proposed algorithm is reduced to 1/12 of the full search algorithm while its performance degradation is 0.03~0.12 dB.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.602-605
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• 2004

A novel motion vector re-estimation technique for transcoding into lower spatial resolution is proposed. This technique is based on the fact that the block matching error is proportional to the complexity of the reference block with Taylor series expansion. It is shown that the motion vectors re-estimated by the proposed method are closer to optimal ones and offer better quality than those of previous techniques.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.999-1002
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• 2002

In this paper, we proposes an adaptive method for reducing the computational overhead of fine-to-coarse MRME at the finest resolution level by considering for the spatial and spectral characteristics between wavelet decomposition levels simultaneously. As we know, there is high correlation between the adjacent blocks and it can give the very important clue to estimate motion at finest level. So, in this paper, using the initial motion vector and the adjacent motion vector in the coarsest level, we determine the optimal direction that will be minimized the estimation error in the finest level. In that direction, we define the potential searching region within the full searching region that is caused to increase much computational overhead in the FtC method. Last, in that region, we process the efficient 2-step motion estimation. and estimate the motion vector at finest resolution level. And then, this determined motion vector is scaled to coarser resolutions. As simulation result, this method is similar to computational complexity of the CtF MRME method and very significantly reduces that of the FtC MRME method. In addition, they provide higher quality than CtF MRME, both visually and quantitatively

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.164-173
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• 2013

In this paper, we propose multi-frame based super resolution algorithm by using motion vector normalization and edge pattern analysis. Existing algorithms have constraints of sub-pixel motion and global translation between frames. Thus, applying of algorithms is limited. And single-frame based super resolution algorithm by using discrete wavelet transform which robust to these problems is proposed but it has another problem that quantity of information for interpolation is limited. To solve these problems, we propose motion vector normalization and edge pattern analysis for 2*2 block motion estimation. The experimental results show that the proposed algorithm has better performance than other conventional algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.49-51
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• 2021

Versatile Video Coding(VVC)에서 동영상 압축 효율을 증가시키기 위한 다양한 화면 간 예측(inter prediction) 기법 중에 적응적 움직임 벡터 해상도(Adaptive motion vector resolution, 이하 AMVR) 기술이 채택되었다. 다만 AMVR을 위해서는 다양한 움직임 벡터 해상도를 테스트해야 하는 부호화 복잡도를 야기하였다. AMVR의 부호화 복잡도를 줄이기 위하여, 본 논문에서는 가벼운 신경망 모델 기반의 AMVR 조기 판별 기법을 제안한다. 이에 따라 불필요한 상황을 미리 조기에 인지하여 대응한다면 나머지 AMVR 과정을 생략할 수 있기에 부호화 복잡도의 향상을 볼 수 있다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.4
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• pp.104-115
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• 1993

The motion compensated coding (MCC) technique, which exploits the temporal redundancies in the moving images with the motion estimation technique,is one of the most popular techniques currently used. Recently, a variable block size(VBS) motion estimation scheme has been utilized to improve the performance of the motion compensted coding. This scheme allows large blocks to the used when smaller blocks provide little gain, saving rates for areas containing more complex motion. Hence, a new VBS motion estimation scheme with a hierarchical structure is proposed in this paper, in order to combine the motion vector coding technique efficiently. Topmost level motion vector, which is obtained by the gain/cost motion estimation technique with selective motion prediction method, is always transmitted. Thus, the hierarchical VBS motion estimation scheme can efficiently exploit the redundancies among neighboring motion vectors, providing an efficient motion vector encoding scheme. Also, a restricted search with respect to the topmost level motion vector enables more flexible and efficient motion estimation for the remaining lower level blocks. Computer simulations on the high resolution image sequence show that, the VBS motion estimation scheme provides a performance improvement of 0.6~0.7 dB, in terms of PSNR, compared to the fixed block size motion estimation scheme.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.305-312
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• 2020

As a demand for a new video coding standard having higher coding efficiency than the existing standards is growing, recently, MPEG and VCEG has been developing and standardizing the next-generation video coding project, named Versatile Video Coding (VVC). Many inter prediction techniques have been introduced to increase the coding efficiency, and among them, an adaptive motion vector resolution (AMVR) technique has contributed on increasing the efficiency of VVC. However, the best motion vector can only be determined by computing many rate-distortion costs, thereby increasing encoding complexity. It is necessary to reduce the complexity for real-time video broadcasting and streaming services, but it is yet an open research topic to reduce the complexity of AMVR. Therefore, in this paper, an efficient technique is proposed, which reduces the encoding complexity of AMVR. For that, the proposed method exploits a special VVC tree structure (i.e., multi-type tree structure) to accelerate the decision process of AMVR. Experiment results show that the proposed decision method reduces the encoding complexity of VVC test model by 10% with a negligible loss of coding efficiency.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1095-1102
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• 2014

In this paper, We proposed the approach of GPU and motion vector to analysis the Human activity in real-time surveillance system. The most important part, that is detect blob(human) in the foreground. We use to detect Adaptive Gaussian Mixture, Weighted subtraction image for salient motion and motion vector. And then, We use motion vector for human activity analysis. In this paper, the activities of human recognize and classified such as meta-classes like this {Active, Inactive}, {Position Moving, Fixed Moving}, {Walking, Running}. We created approximately 300 conditions for the simulation. As a result, We showed a high success rate about 86~98%. The results also showed that the high resolution experiment by the proposed GPU-based method was over 10 times faster than the cpu-based method.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.887-890
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• 1998

We present a novel multi-resolution block matching algorithm (BMA) for fast motion estimation. At the coarsest level, a full search BMA (FSBMA) is performed for searching complex or random motion. Concurrently, spatial correlation of motion vector (MV) field is used for searching continuous motion. Here we present an efficient method for searching full resolution MVs without MV decimation even at the coarsest leve. After the coarsest level search, two or three initial MV candidates are chosen for the next level. At the further levels, the MV candidates are refined within much smaller search areas. Simulation results show that in comparison with FSBMA, the proposed BMA achieves a speed-up factor over 710 with minor PSNR degradation of 0.2dB at most, under a normal MPEG2 coding environment. Furthermore, our scheme is also suitable for hardware implementation due to regular data-flow.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8C
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• pp.824-831
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• 2005

In the reconstruction of high resolution (HR) images from low resolution (LR) images frames, the error in the estimated motion degrades the reliability of the reconstructed HR image. Some methods were recently proposed where motion estimation and HR reconstruction is performed simultaneously. The estimated motion is still prone to error when it is based on a simple block matching. In this paper, we propose a reconstruction of a HR image by applying POCS and a regularized block matching simultaneously. In this method, a motion vector is obtained from a regularized block matching algorithm since the motion of a pixel in an image is highly correlated with the motion in neighboring regions. Experimental results show that the improved accuracy of the estimated motion vectors results in higher PSNR of the reconstructed HR images.