• Journal of Broadcast Engineering
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• v.7 no.3
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• pp.238-247
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• 2002

In this paper, we propose an object-based stereo sequence compression technique using disparity-motion vector relationship. The proposed method uses the coherence of motion vectors and disparity vectors in the left and right Image sequences. After two motion vectors and one disparity vector ate computed using FBMA(Fixed Block Matching Algorithm), the disparity vector of the current stereoscopic pall is computed by disparity-motion vector relationship with vectors which are previously estimated. Moreover, a vector regularization technique is applied in order to obtain reliable vectors. For an object-based coding. the object is defined and coded in terms of layers of VOP such as in MPEG-4. we present a method using disparity and motion vector relationship for extending two-frame compensation into three-frame compensation method for prediction coding of B-VOP. The proposed algorithm shows a high performance when comparing with a conventional method.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.3
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• pp.118-127
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• 2014

This paper presents an efficient motion and disparity prediction method for multi-view video coding based on the high efficient video coding (HEVC) standard. The proposed method exploits inter-view candidates for effective prediction of the motion or disparity vector to be coded. The inter-view candidates include not only the motion vectors of adjacent views, but also global disparities across views. The motion vectors coded earlier in an adjacent view were found to be helpful in predicting the current motion vector to reduce the number of bits used in the motion vector information. In addition, the proposed disparity prediction using the global disparity method was found to be effective for interview predictions. A multi-view version based on HEVC was used to evaluate the proposed algorithm, and the proposed correspondence prediction method was implemented on a multi-view platform based on HEVC. The proposed algorithm yielded a coding gain of approximately 2.9% in a high efficiency configuration random access mode.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.2
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• pp.37-48
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• 2008

Generally, a motion vector and a disparity vector represent the motion information of an object in a single-view of camera and the displacement of the same scene between two cameras that located spatially different from each other, respectively. Conventional H.264/AVC does not use the disparity vector in the motion vector prediction because H.264/AVC has been developed for the single-view video. But, multi-view video coding that uses the inter-view prediction structure based on H.264/AVC can make use of the disparity vector instead of the motion vector when the current frame refers to the frame of different view. Therefore, in this paper, we propose an improved motion/disparity vector prediction method that consists of global disparity vector replacement and extended neighboring block prediction. From the experimental results of the proposed method compared with the conventional motion vector prediction of H.264/AVC, we achieved average 1.07% and 1.32% of BD (Bjontegaard delta)-bitrate saving for ${\pm}32$ and ${\pm}64$ of global vector search range, respectively, when the search range of the motion vector prediction is set to ${\pm}16$.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1203-1204
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• 2008

In this paper, a new stereo object tracking system using the disparity motion vector is proposed. In the proposed method, the time-sequential disparity motion vector can be estimated from the disparity vectors which are extracted from the sequence of the stereo input image pair and then using these disparity motion vectors, the area where the target object is located and its location coordinate are detected from the input stereo image. Basing on this location data of the target object, the pan/tilt embedded in the stereo camera system can be controlled and as a result, 3D tracking of the target object can be possible.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.56-65
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• 2009

Stereoscopic images consist of the left image and the right image. Thus, stereoscopic images have much amounts of data than single image. Then an efficient image compression technique is needed, the DPCM-based predicted coding compression technique is used in most video coding standards. Motion and disparity estimation are needed to realize the predicted coding compression technique. Their performing algorithm is block matching algorithm used in most video coding standards. Full search algorithm is a base algorithm of block matching algorithm which finds an optimal block to compare the base block with every other block in the search area. This algorithm presents the best efficiency for finding optimal blocks, but it has very large computational loads. In this paper, we have proposed fast disparity estimation algorithm using motion and disparity vector information of the prior frame in stereo image coding. We can realize fast disparity vector estimation in order to reduce search area by taking advantage of global disparity vector and to decrease computational loads by limiting search points using motion vectors and disparity vectors of prior frame. Experimental results show that the proposed algorithm has better performance in the simple image sequence than complex image sequence. We conclude that the fast disparity vector estimation is possible in simple image sequences by reducing computational complexities.

• Journal of Information Display
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• v.6 no.4
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• pp.16-25
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• 2005

In this paper, a new 3D object tracking system using the disparity motion vector (DMV) is presented. In the proposed method, the time-sequential disparity maps are extracted from the sequence of the stereo input image pairs and these disparity maps are used to sequentially estimate the DMV defined as a disparity difference between two consecutive disparity maps Similarly to motion vectors in the conventional video signals, the DMV provides us with motion information of a moving target by showing a relatively large change in the disparity values in the target areas. Accordingly, this DMV helps detect the target area and its location coordinates. Based on these location data of a moving target, the pan/tilt embedded in the stereo camera system can be controlled and consequently achieve real-time stereo tracking of a moving target. From the results of experiments with 9 frames of the stereo image pairs having 256x256 pixels, it is shown that the proposed DMV-based stereo object tracking system can track the moving target with a relatively low error ratio of about 3.05 % on average.

• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.240-252
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• 2017

Multi-view video codec based on the High Efficiency Video Coding (MV-HEVC) has high encoding complexity because it exploits an additional reference picture for disparity compensation prediction (DCP) when the picture of dependent view is encoded. In this paper, we propose an efficient method to reduce the complexity of disparity motion vector search for the MV-HEVC. The proposed method includes the initial search point decision method using affine transform and the adaptive search range decision method. The simulation results show that the proposed method reduces the complexity of disparity motion vector search up to 90.78% with negligible coding efficiency degradation. Also the results show that the proposed method outperforms other conventional techniques reducing complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.2080-2089
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• 2015

In 3D video compression, a disparity vector (DV) pointing a corresponding block position in an adjacent view is a key coding tool to exploit statistical correlation in multi-view videos. In this paper, neighboring block-based disparity vector (NBDV) is shown with detail algorithm descriptions and coding performance analysis. The proposed method derives a DV from disparity motion vector information, obtained from spatially and temporally neighboring blocks, and provides a significant coding gain about 20% BD-rate saving in a texture-video-first-coding scheme. The proposed DV derivation method is adopted into the recent 3D video coding standards such as 3D-AVC and 3D-HEVC as the state-of-the-art DV derivation method.