• 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 institute of electronic communication sciences
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• v.5 no.1
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• pp.23-30
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• 2010

The H.264/AVC is increasingly used in broadcast video applications such as Internet Protocol television (IPTV), digital multimedia broadcasting (DMB) because of high compression performance. But the H.264/AVC coded video can be delivered to the widespread end-user equipment for MPEG-2 after transcoding between this video standards. This paper suggests a new transcoding algorithm for H.264/AVC to MPEG-2 transcoder that uses motion vector clustering in order to reduce the complexity without loss of video quality. The proposed method is exploiting the motion information gathered during h.264 decoding stage. To reduce the search space for the MPEG-2 motion estimation, the predictive motion vector is selected with a least distortion of the candidated motion vectors. These candidate motion vectors are considering the correlation of direction and distance of motion vectors of variable blocks in H.264/AVC. And then the best predictive motion vector is refined with full-search in ${\pm}2$ pixel search area. Compared with a cascaded decoder-encoder, the proposed transcoder achieves computational complexity savings up to 64% with a similar PSNR at the constant bitrate(CBR).

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.1006-1009
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• 2003

In a motion estimation method for use in encoding a moving picture, a full-pixel motion vector is estimated by stochastically sampling a pixel to be processed in a predetermined-sized block of a previous frame or a next frame as a reference frame for each of a plurality of equal-sized blocks in a current frame. Then, a half-pixel motion vector is estimated based on the full-pixel motion vector. Accordingly, both the calculation amount and the calculation time required for the motion estimation are effectively reduced. Further, it can be prevented that the hardware becomes complicated. .

• 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 Korea Computer Graphics Society
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• v.29 no.3
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• pp.159-166
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• 2023

This paper investigates effective methods for implementing motion matching, which is actively used in real-time motion generation applications. The success of motion matching heavily hinges on its simple definition of a feature vector, yet this very definition can introduce significant variance in the outcomes. Our research focuses on identifying the optimal combination of feature vectors that effectively generates desired trajectories in locomotion generation. To this end, we experimented with a range of feature vector combinations and performed an in-depth error analysis to evaluate the results.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.63-68
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• 1998

We present a fast adaptive block matching algorithm using characteristic of the motion vector distribution. In the presented method, the block is classified into one of four motion categories: stationary block, quasi-stationary block, medium-motion block or high-motion block according to characteristic of the MAD(0,0) distribution for motion vector, each block estiamtes the motion vector adaptively. By the simulation, the PSNR of our algorithm is similar to NTSS method. The computation amount of the presented method decreased 30.44% ~ 40.27% more than NTSS method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.494-503
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• 2002

A motion vector re-estimation algorithm for image downscaling in discrete consine transform domain is presented. Kernel functions are difined using SAD (Aum of Absolute Difference) and edge information of a macroblock. The proposed method uses these kernel functions to re-estimate a new motion vector of the downscaled image. The motion vectors from the incoming bitstream of transcoder are reused to reduce computation burden of the block-matching motion estimation, and we also reuse the given motion vectors. Several experiments in this paper show that the computation efficiency and the PSNR (Peak Signal to Noise Ratio) and better than the previous methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.12C
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• pp.1669-1676
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• 2004

In H.264, 7 modes {16${\times}$16, 16${\times}$8, 8${\times}$16, 8${\times}$8, 8${\times}$4, 4${\times}$8, 4${\times}$4) are used to enhance the coding efficiency. The motion vector estimation with 7 modes may require huge computing time. In this paper, to speed up the motion vector estimation procedure while the high image quality remains, we propose a motion vector refinement scheme using the temporary motion vector generated with little computation. The proposed estimation process consists of three phases: Mode decision for a 16${\times}$16 macroblock, Composing a temporary motion vector, Refinement of the temporary motion vector. We demonstrate the effectiveness of the proposed method by computer simulation. In the results, the encoding time consumed by the proposed scheme has been reduced significantly while the encoded video quality remains unchanged.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.801-808
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• 2007

Video coding standards such as MPEG-2, MPEG-4, H.263, and H.264 transmit a compressed video data using wired/wireless communication line with limited bandwidth. Because highly compressed bit-streams is likely to fragile to error from channel noise, video is damaged by error. There have been many research works on error concealment techniques, which recover transmission errors at decoder side [1, 2]. We designed an error concealment technique for lost motion vectors of H.264 video coding. In this paper, we propose a Kalman filter based motion vector recovery scheme, and experimented with standard video sequences. The experimental results show that our scheme restores original motion vector with more precision of 0.91 - 1.12 on average over conventional H.264 decoding with no error recovery.

• Journal of the Korea Computer Industry Society
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• v.2 no.8
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• pp.1075-1088
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• 2001

In this paper, we propose a method for dynamic selection of unrestricted motion vector(UMV) or default prediction mode(DPM) in H.263 bit stream. For this, we use the error of compensated image and the magnitude of motion vector. In the proposed strategy, the UMV mode is dynamically applied in a frame according to average magnitude of motion vector and error of compensated image. This scheme has improved the quality of image compared to the fixed mode UMV or DPM only. Number of searching points are greatly reduced when comparing to UMV The proposed method is more profitable to long video sequences having camera movement locally.