Multiview Video Coding Using View Interpolation and Color Correction
ABSTRACT Neighboring views must be highly correlated in multiview video systems. We should therefore use various neighboring views to efficiently compress videos. There are many approaches to doing this. However, most of these treat pictures of other views in the same way as they treat pictures of the current view, i.e., pictures of other views are used as reference pictures (inter-view prediction). We introduce two approaches to improving compression efficiency in this paper. The first is by synthesizing pictures at a given time and a given position by using view interpolation and using them as reference pictures (view-interpolation prediction). In other words, we tried to compensate for geometry to obtain precise predictions. The second approach is to correct the luminance and chrominance of other views by using lookup tables to compensate for photoelectric variations in individual cameras. We implemented these ideas in H.264/AVC with inter-view prediction and confirmed that they worked well. The experimental results revealed that these ideas can reduce the number of generated bits by approximately 15% without loss of PSNR.
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ABSTRACT: Color correction between two or multiple images is very crucial for the development of subsequent algorithms and stereoscopic 3D camera system. Even though various color correction methods are proposed recently, there are few methods for measuring the performance of these methods. In addition, when two images have view variation by camera positions, previous methods for the performance measurement may not be appropriate. In this paper, we propose a method of measuring color difference between corresponding images for color correction. This method finds matching points that have the same colors between two scenes to consider the view variation by correspondence searches. Then, we calculate statistics from neighbor regions of these matching points to measure color difference. From this approach, we can consider misalignment of corresponding points contrary to conventional geometric transformation by a single homography. To handle the case that matching points cannot cover the whole regions, we calculate statistics of color difference from the whole image regions. Finally, the color difference is computed by the weighted summation between correspondence based and the whole region based approaches. This weight is determined by calculating the ratio of occupying regions by correspondence based color comparison.Journal of Broadcast Engineering. 03/2012; 17(2).
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ABSTRACT: In stereo-view system, variations of target camera position or lighting conditions cause discrepancies on the luminance and chrominance components of stereo views. These discrepancies lead to inaccurate frame view prediction and low quality of 3 D video coding. In this paper, an efficient histogram interval calibration method is proposed for stereo-view coding, so as to compensate for the luminance component of target view. First the proposed method is analyzed by the histogram of the target image frame. Then, it divide two sections of histogram of that frame to correct the color discrepancies. Secondly, each section of the target frame is corrected the luminance component by identify the maximum matching region between the reference frame and the target frame. We have verified our proposed histogram matching method in comparison with the other color correction ones. Experimental results show that it can correct better luminance calibration results of PSNR(Peak Signal to Noise Ratio) and has less computation time.Journal of the Institute of Electronics and Information Engineers. 01/2013; 50(12).
Conference Paper: Joint complexity and rate optimization for 3DTV depth map encoding[Show abstract] [Hide abstract]
ABSTRACT: Current research towards 3D video compression within MPEG requires the compression of three texture and depth views. To reduce the additional complexity and bit rate of the depth map encoding, we present a fast mode decision model based on previously encoded macroblocks of the texture view. Meanwhile we present techniques to reduce the rate based on predicting syntax elements from the corresponding texture view. The proposed system is able to get a reduction in complexity of 71.08% with an average bit rate gain of 4.35%.Consumer Electronics (ICCE), 2013 IEEE International Conference on; 01/2013