• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.242-259
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• 2015

Three-dimensional video coding is one of the main challenges restricting the widespread applications of 3D video and free viewpoint video. In this paper, a novel fractal coding algorithm with motion-vector-field-based motion estimation for depth map sequence is proposed. We firstly add pre-search restriction to rule the improper domain blocks out of the matching search process so that the number of blocks involved in the search process can be restricted to a smaller size. Some improvements for motion estimation including initial search point prediction, threshold transition condition and early termination condition are made based on the feature of fractal coding. The motion-vector-field-based adaptive hexagon search algorithm on the basis of center-biased distribution characteristics of depth motion vector is proposed to accelerate the search. Experimental results show that the proposed algorithm can reach optimum levels of quality and save the coding time. The PSNR of synthesized view is increased by 0.56 dB with 36.97% bit rate decrease on average compared with H.264 Full Search. And the depth encoding time is saved by up to 66.47%. Moreover, the proposed fractal depth map sequence codec outperforms the recent alternative codecs by improving the H.264/AVC, especially in much bitrate saving and encoding time reduction.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.428-435
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• 2017

In this paper, we propose a novel motion field estimation algorithm for which a U-disparity map and forward-and-backward error removal are applied in a vehicular environment. Generally, a motion exists in an image obtained by a camera attached to a vehicle by vehicle movement; however, the obtained motion vector is inaccurate because of the surrounding environmental factors such as the illumination changes and vehicles shaking. It is, therefore, difficult to extract an accurate motion vector, especially on the road surface, due to the similarity of the adjacent-pixel values; therefore, the proposed algorithm first removes the road surface region in the obtained image by using a U-disparity map, and uses then the optical flow that represents the motion vector of the object in the remaining part of the image. The algorithm also uses a forward-backward error-removal technique to improve the motion-vector accuracy and a vehicle's movement is predicted through the application of the RANSAC (RANdom SAmple Consensus) to the previously obtained motion vectors, resulting in the generation of a motion field. Through experiment results, we show that the performance of the proposed algorithm is superior to that of an existing algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2343-2352
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• 2015

In this paper, we propose novel motion field estimation method using U-disparity map and forward-backward error removal in vehicles environment. Generally, in an image obtained from a camera attached in a vehicle, a motion vector occurs according to the movement of the vehicle. but this motion vector is less accurate by effect of surrounding environment. In particular, it is difficult to extract an accurate motion vector because of adjacent pixels which are similar each other on the road surface. Therefore, proposed method removes road surface by using U-disparity map and performs optical flow about remaining portion. forward-backward error removal method is used to improve the accuracy of the motion vector. Finally, we predict motion of the vehicle by applying RANSAC(RANdom SAmple Consensus) from acquired motion vector and then generate motion field. Through experimental results, we show that the proposed algorithm performs better than old schemes.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.28-35
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• 2010

The variable block size motion estimation (ME) and compensation (MC) using multiple reference frames is adopted in H.264/AVC to improve coding efficiency. However, the computational complexity for ME/MC increases proportional to the number of reference frames and variable blocks. In this paper, we propose a new efficient reference frame selection algorithm to reduce the complexity while keeping the visual quality. First, a motion vector reference map is constructed by SAD of $4{\times}4$ block unit for multi reference frames. Next, the variable block size motion estimation and motion compensation is performed according to the motion vector reference map. The computer simulation results show that the average loss of BDPSNR is -0.01dB, the increment of BDBR is 0.27%, and the encoding time is reduced by 38% compared with the original method for H.264/AVC.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1411-1418
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• 1999

In this paper, we propose a fast motion estimation algorithm using motion prediction and neural network. Considering that the motion vectors have high spatial correlation, the motion vector of current block is predicted by those of neighboring blocks. The codebook of motion vector is designed by Kohonen self-organizing feature map(KSFM) learning algorithm which has a fast learning speed and 2-D adaptive chararteristics. Since the similar codevectors are closely located in the 2-D codebook the motion is progressively estimated from the predicted codevector in the codebook. Computer simulation results show that the proposed method has a good performance with reduced computational complexity.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.48-56
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• 2009

We propose a fast intra mode skip decision algorithm for H.264/AVC video encoding. Although newly added MB encoding algorithms based on various prediction methods increase compression ratio, they require a significant increase in the computational complexity because we calculate rate-distortion(RD) cost for all possible MB coding modes and then choose the best one. In this paper, we propose a fast mode decision algorithm based on an adaptive motion vector map(AMVM) method for H.264/AVC video encoding to reduce the processing time for the inter frame. We verify that the proposed algorithm generates generally good performances in PSNR, bit rates, and processing time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4587-4605
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• 2019

High Efficiency Video Coding (HEVC) suffers from high computational complexity due to its quad-tree structure in motion estimation (ME). This paper exposes an adaptive search range decision algorithm for accelerating HEVC integer-pel ME on GPU which estimates the optimal search range (SR) using a MAP (Maximum A Posteriori) estimator. There are three main contributions; First, we define the motion feature as the standard deviation of motion vector difference values in a CTU. Second, a MAP estimator is proposed, which theoretically estimates the motion feature of the current CTU using the motion feature of a temporally adjacent CTU and its SR without any data dependency. Thus, the SR for the current CTU is parallelly determined. Finally, the values of the prior distribution and the likelihood for each discretized motion feature are computed in advance and stored at a look-up table to further save the computational complexity. Experimental results show in conventional HEVC test sequences that the proposed algorithm can achieves high average time reductions without any subjective quality loss as well as with little BD-bitrate increase.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.147-149
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• 2011

사용자에게 편리한 인터페이스를 제공하는 IT 기기가 널리 보급되면서 직관적인 인터페이스 기술에 대한 관심이 높아지고 있다. 이미지 센서로 입력된 사람의 손 모양이나 움직임을 이용하는 인터페이스가 그 중 하나이다. 한 편 이미지 센서 영상을 저장하기 위하여 H.264/AVC와 같은 영상 압축 기술이 사용된다. 영상을 압축하기 위해 부호기는 모든 Macroblock에서 움직임 추정을 수행한다. 추정된 움직임 정보는 손 움직임을 인식하는데 사용될 수 있고 이를 통해 전자 기기에 명령을 내리는 인터페이스 기술의 한 부분을 구현하는 것이 가능하다. 본 논문은 H.264/AVC 부호기의 Motion Vector를 이용하는 손 추적 알고리즘을 제시한다. 제시된 알고리즘은 손 움직임 추적의 정확도를 향상시키기 위하여 Motion Vector보다 신뢰도가 높은 Motion Density Map 정보를 사용한다. 이 정보를 이용하여 이동한 손을 포함하는 손 윈도우를 결정한다. 실험 결과를 통하여 제시된 알고리즘이 손의 움직임을 추적하는 것을 확인할 수 있다.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.5
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• pp.317-321
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• 2013

This paper proposes a detection algorithm for view reversal in a stereoscopic video using a disparity map and motion vector field. We obtain the disparity map of a stereo image was obtained using a specific stereo matching algorithm and classify the image into the foreground and background. Next, the motion vector field of the image on a block basis was produced using a full search algorithm. Finally, the stereo image was considered to be reversed when the foreground moved toward the background and the covered region was in the foreground. The proposed algorithm achieved a good detection rate when the background was covered sufficiently by its moving foreground.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.965-971
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• 2015

This paper suggests a feature point-based Iterative Closest Point (ICP) algorithm to compensate for the disparity error in building a two-dimensional map. The ICP algorithm is a typical algorithm for matching a common object in two different images. In the process of building a two-dimensional map using the laser scanner data, warping and distortions exist in the map because of the disparity between the two sensor values. The ICP algorithm has been utilized to reduce the disparity error in matching the scanned line data. For this matching process in the conventional ICP algorithm, pre-known reference data are required. Since the proposed algorithm extracts characteristic points from laser-scanned data, reference data are not required for the matching. The laser scanner starts from the right side of the mobile robot and ends at the left side, which causes disparity in the scanned line data. By finding the matching points between two consecutive frame images, the motion vector of the mobile robot can be obtained. Therefore, the disparity error can be minimized by compensating for the motion vector caused by the mobile robot motion. The validity of the proposed algorithm has been verified by comparing the proposed algorithm in terms of map-building accuracy to conventional ICP algorithm real experiments.