Mr. Jianxin Wang Profile

Jianxin Wang

at Shanghai Univ

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Proceedings Article | 31 October 2014 Paper

High accuracy hole filling for Kinect depth maps

Jianxin Wang, Ping An, Yifan Zuo, Zhixiang You, Zhaoyang Zhang

Proceedings Volume 9273, 92732L (2014) https://doi.org/10.1117/12.2071437

KEYWORDS: Cameras, Digital filtering, Image segmentation, Image processing, RGB color model, Optical filters, Image filtering, Sensors, Image enhancement, Infrared lasers

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Hole filling of depth maps is a core technology of the Kinect based visual system. In this paper, we propose a hole filling algorithm for Kinect depth maps based on separately repairing of the foreground and background. There are two-part processing in the proposed algorithm. Firstly, a fast pre-processing to the Kinect depth map holes is performed. In this part, we fill the background holes of Kinect depth maps with the deepest depth image which is constructed by combining the spatio-temporal information of the pixels in Kinect depth map with the corresponding color information in the Kinect color image. The second step is the enhancement for the pre-processing depth maps. We propose a depth enhancement algorithm based on the joint information of geometry and color. Since the geometry information is more robust than the color, we correct the depth by affine transform in prior to utilizing the color cues. Then we determine the filter parameters adaptively based on the local features of the color image which solves the texture copy problem and protects the fine structures. Since L1 norm optimization is more robust to data outliers than L2 norm optimization, we force the filtered value to be the solution for L1 norm optimization. Experimental results show that the proposed algorithm can protect the intact foreground depth, improve the accuracy of depth at object edges, and eliminate the flashing phenomenon of depth at objects edges. In addition, the proposed algorithm can effectively fill the big depth map holes generated by optical reflection.

Proceedings Article | 29 October 2014 Paper

Joint bit allocation for 3D video coding based on virtual view distortion

Chao Yang, Ping An, Jianxin Wang, Zhaoyang Zhang

Proceedings Volume 9273, 92730N (2014) https://doi.org/10.1117/12.2070924

KEYWORDS: Distortion, Volume rendering, Image compression, Video coding, Quantization, Video, 3D video compression, 3D image processing, Video compression, Communication engineering

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In multi-view plus depth (MVD) 3D video coding, texture maps and depth maps are coded jointly. The depth maps provide the scene geometry information and are used to render the virtual view at the terminal through a Depth-Image-Based-Rendering (DIBR) technique. The distortion of the coded texture maps and depth maps will induce synthesized virtual view distortion. Besides the coding efficiency of texture maps and depth maps, bit allocation between texture maps and depth maps also has a great effect on the virtual view quality. In this paper, the virtual view distortion is divided into texture maps induced distortion and depth maps induced distortion separately, models of texture maps induced virtual view distortion and depth maps induced virtual view distortion are derived respectively. Based on the depth maps induced virtual view distortion model, depth maps coding Rate Distortion Optimization (RDO) is modified and the depth maps coding efficiency is increased. Meanwhile, we also propose a Rate-distortion (R-D) model to solve the joint bit allocation problem. Experimental results demonstrate the high accuracy of the proposed virtual view distortion model. The R-D performance of the proposed algorithm is close to the full search algorithm that can give the best R-D performance, while the coding complexity of the proposed algorithm is lower. Compared with fixed texture and depth bits ratio (5:1), an average 0.3 dB gains can be achieved by the proposed algorithm. The proposed algorithm has high rate control accuracy with the average error less than 1%.

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