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.
Article: Multiview Coding using AVC
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ABSTRACT: After , , , , , , minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.IEEE Transactions on Pattern Analysis and Machine Intelligence 10/2004; 26(9):1124-37. · 4.80 Impact Factor