• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.371-374
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• 2001

본 논문에서는 움직임 보상을 이용한 Motion-Adaptive De-interlacing Method를 제안 한다. 정확한 움직임 추정을 위해서 Pre-filter로서 EBMF(Edge Based Median Filter)를 사용하며 새로운 Block Matching Method를 제안한다. Temporal Filter로서 Motion Missing Error를 제거하기 위해 입력 영상의 움직임 영역에 따라 각각 다른 임계 값을 적용하는 AMPDF(Adaptive Minimum Pixel Difference Filter)를 적용하였으며 MMD(Maximum Motion Detection)와 SAD(Sum of Difference)를 이용하여 빠른 움직임 영역에서 화질을 향상시켰다. 최종적으로 잘못된 움직임 보상에 기인하는 화질의 열화를 방지하기 위한 Motion Correction Filter를 제안한다.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.188-191
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• 2002

To reduce the bit-rate of video sequences by removing temporal redundancy, motion estimation techniques have been developed. However, the high computational complexity of the problem makes such techniques very difficult to be applied to high-resolution applications in a real time environment. For this reason, low computational complexity motion estimation algorithms are viable solutions. If a priori knowledge about the motion of the current block is available before the motion estimation, a better starting point for the search of n optimal motion vector on be selected and also the computational complexity will be reduced. In this paper, we present an adaptive block matching algorithm based on temporal correlations of consecutive image frames that defines the search pattern and the location of initial starting point adaptively to reduce computational complexity. Experiments show that, comparing with DS(Diamond Search) algorithm, the proposed algorithm is about 0.1∼0.5(㏈) better than DS in terms of PSNR and improves as much as 50% in terms of the average number of search points per motion estimation.

• 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.

• 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 Korea Multimedia Society
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• v.13 no.7
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• pp.991-999
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• 2010

In this paper, we propose a fast motion estimation algorithm for video encoding. Conventional fast motion estimation algorithms have a serious problem of low prediction quality in some frames. However, full search based fast algorithms have low computational reduction ratio. In the paper, we propose an algorithm that significantly reduces unnecessary computations, while keeping prediction quality almost similar to that of the full search. The proposed algorithm uses distribution probability of motion vectors and adaptive search patterns and block matching criteria. By taking different search patterns and error criteria of block matching according to distribution probability of motion vectors, we can reduces only unnecessary computations efficiently. Our algorithm takes only 20~30% in computational amount and has decreased prediction quality about 0~0.02dB compared with the fast full search of the H.264 reference software. Our algorithm will be useful to real-time video coding applications using MPEG-2 or MPEG-4 AVC standards.

• Journal of Broadcast Engineering
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• v.7 no.4
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• pp.310-316
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• 2002

In this paper, we propose an adaptive search range decision algorithm for fast motion estimation of video coding. Block matching algorithm for motion vector estimation that improves coding efficiency by reduction of temporal redundancy has trade-off problem between the motion vector accuracy and the complexity. The proposed algorithm playing as a pre-processing of fast motion estimation adaptively determines the motion search range by the local statistics of neighboring motion vectors. resulting in dramatic reduction of the computational cost without the loss of coding efficiency. Experimental results show the capability of the proposed algorithm.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.410-421
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• 2019

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1700-1721
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• 2017

The problem of blocking artifacts is very common in block-based image and video compression, especially at very low bit rates. In this paper, we propose a post-processing method for JPEG-coded image deblocking via sparse representation and adaptive residual threshold. This method includes three steps. First, we obtain the dictionary by online dictionary learning and the compressed images. The dictionary is then modified by the histogram of oriented gradient (HOG) feature descriptor and K-means cluster. Second, an adaptive residual threshold for orthogonal matching pursuit (OMP) is proposed and used for sparse coding by combining blind image blocking assessment. At last, to take advantage of human visual system (HVS), the edge regions of the obtained deblocked image can be further modified by the edge regions of the compressed image. The experimental results show that our proposed method can keep the image more texture and edge information while reducing the image blocking artifacts.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1925-1938
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• 2001

In this paper, a new block-matching algorithm for standard video encoder is presented. The slice competition method is proposed as a new scheme, as opposed to a coarse-to-fine approach. The order of calculating the SAD(Sum of Absolute Difference) to fad the best matching block is changed from a raster order to a dispersed one. Based on this scheme, the increasing SAD curve during its calculation is more linear than that of other curves. Then, the candidates of low probability can be removed in the early stage of calculation. And new MV prediction technique with an adaptive search range scheme also assists the proposed block-matching algorithm. As a result, an average of 13% improvement in computational power is recorded by only the proposed MV prediction technique. Synthetically, the computational power is reduced by 3977∼77% than that of the conventional BMAs. The average MAD is always low in various sequences. The results are also very close to the MAD of the full search block-matching algorithm.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.810-813
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• 2007

Recently, mobile video communication is getting more and more popular. Visual quality and computational complexity are primary factors affecting performance of video communication. Frame rate up-conversion (FRC) is necessary for achieving high visual quality in mobile projection displays. In this paper, a FRC method using motion compensation based on block matching algorithm (BMA) with adaptive block size is proposed. In order to improve the accuracy of the estimated motion vectors, the motion vector refinement technique is proposed. Experiment results indicate that the proposed technique exhibits better performance with lower hardware complexity compared to the conventional methods.