• 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.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.768-782
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• 2010

Because of the different shooting position between multi-view cameras and the imperfect camera calibration, Illumination mismatches of multi-view video can happen. This variation can bring about the performance decrease of multi-view video coding(MVC) algorithm. A histogram matching algorithm can be applied to recompensate these inconsistencies in a prefiltering step. Once all camera frames of a multi-view sequence are adjusted to a predefined reference through the histogram matching, the coding efficiency of MVC is improved. However the histogram distribution can be different not only between neighboring views but also between sequential views on account of movements of camera angle and some objects, especially human. Therefore the histogram matching algorithm which references all frames in chose view is not appropriate for compensating the illumination differences of these sequence. Thus we propose new algorithms both the image classification algorithm which is applied two criteria to improve the correlation between inter-view frames and the histogram matching which references and matches with a group of pictures(GOP) as a unit to advance the correlation between successive frames. Experimental results show that the compression ratio for the proposed algorithm is improved comparing with the conventional algorithms.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.261-270
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• 2013

A stereoscopic 3D video service is able to provide a 3D video service while keeping backward compatibility with the existing 2D video service. In the terrestrial digital television (DTV) system, a stereoscopic video codec is required to have high coding efficiency in order to provide a 3D video service in the same channel capacity. A hybrid codec consisting of MPEG-2 for base video and H.264/AVC or HEVC for 3D auxiliary video is considered. Furthermore, Non-Real-Time (NRT) delivery of stereoscopic video is also considered as a service scenario for 3DTV services to overcome the limited bandwidth. In this paper, we propose a stereoscopic video coding scheme using adaptive loop filter (ALF) which had been considered in HEVC as a pre-/post-filter for enhancing coding efficiency in NRT-based 3DTV services. In order to apply ALF as a post-filter to the reconstructed additional view coded by H.264/AVC, we devise a method in which ALF is adaptively applied based on a structure determined by using macroblock (MB) coding information such as MB mode type and reference index instead of coding unit (CU) structure on which ALF is applied in the HEVC. Experimental results shows that the proposed stereoscopic video coding scheme applying ALF obtains up to 24.9% gain of bit saving.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.127-136
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• 2010

In multi-view video, illumination disharmony between neighboring views can occur on account of different location of each camera and imperfect camera calibration, and so on. Such discrepancy can be the cause of the performance decrease of multi-view video coding by mismatch of inter-view prediction which refer to the pictures obtained from the neighboring views at the same time. In this paper, we propose an efficient histogram-based prefiltering algorithm to compensate mismatches between the luminance and chrominance components in multi-view video for improving its coding efficiency. To compensate illumination variation efficiently, all camera frames of a multi-view sequence are adjusted to a predefined reference through the histogram matching. A Cosited filter that is used for chroma subsampling in many video encoding schemes is applied to each color component prior to histogram matching to improve its performance. The histogram matching is carried out in the RGB color space after color space converting from YCbCr color space. The effective color conversion skill that has respect to direction of edge and range of pixel value in an image is employed in the process. Experimental results show that the compression ratio for the proposed algorithm is improved comparing with other methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.651-660
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• 2010

In this paper, we implement an efficient histogram-based prefiltering to compensate the illumination mismatches in regions between neighboring views. In multi-view video, such illumination disharmony can primarily occur on account of different camera location and orientation and an imperfect camera calibration. This discrepancy can cause the performance decrease of multi-view video coding(MVC) algorithm. A histogram matching algorithm can be exploited to make up for these differences in a prefiltering step. Once all camera frames of a multi-view sequence are adjusted to a predefined reference through the histogram matching, the coding efficiency of MVC is improved. However general frames of multi-view video sequence are composed of several regions with different color composition and their histogram distribution which are mutually independent of each other. In addition, the location and depth of these objects from sequeuces captured from different cameras can be different with different frames. Thus we propose a new algorithm which classify a image into several subpartitions by its depth information first and then histogram matching is performed for each region individually. Experimental results show that the compression ratio for the proposed algorithm is improved comparing with the conventional image-based algorithms.