Download Advances on Digital Television and Wireless Multimedia by Chao Jiang, Ze-xun Geng, Xiao-feng Wei, Chen Shen (auth.), PDF

By Chao Jiang, Ze-xun Geng, Xiao-feng Wei, Chen Shen (auth.), Wenjun Zhang, Xiaokang Yang, Zhixiang Xu, Ping An, Qizhen Liu, Yue Lu (eds.)

This ebook constitutes the refereed court cases of the ninth foreign discussion board on electronic television and instant Multimedia verbal exchange, IFTC 2012, Shanghai, China, November. The sixty nine revised complete papers offered have been rigorously reviewed and chosen from a variety of submissions. The papers are geared up in topical sections on snapshot processing and development attractiveness; photograph and video research; snapshot caliber evaluation; textual content picture and speech processing; content material retrieval and defense; resource coding; multimedia conversation; new advances in broadband multimedia; human laptop interface; 3D video.

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Read Online or Download Advances on Digital Television and Wireless Multimedia Communications: 9th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2012, Shanghai, China, November 9-10, 2012. Proceedings PDF

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Extra resources for Advances on Digital Television and Wireless Multimedia Communications: 9th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2012, Shanghai, China, November 9-10, 2012. Proceedings

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3 shows two synthesized HDR images and their original LDR image sequences. It can be observed that the delicate textures of the sea surface, the ground and the clouds make these HDR images have pleasing visual effects. Fig. 4 shows the uniform distribution of the selected points for CRF recovery in gray level space, and CRFs recovered for each channel in a color image. Fig. 3. HDR images displayed on an LDR device. Left: An HDR image that records the details from the darkest shadows to the brightest sun with its original sequence; Right: An HDR image that clearly records the brightest sun as well as the darkest ground and the original sequence.

1. Illustration of propagation. The step length is halved. , the red patch, inquiries eight blue 8-neighbors, and takes fi (x + w, y + h)(i = 1 · · · k, w ∈ {−l, 0, l}, h ∈ {−l, 0, l}) as candidate offsets of kNN. efficiency, we build a max-heap to store the patch distances D of the k-nearest neighbors of each effective patch in A. To avoid testing a patch already in the k-nearest list, a hash table is constructed for each patch in A. Propagation: Propagation is to improve initial kNNF, which is far from the exact kNNF.

Each column of eigenvectors U represents a basis function of the transformation. Supposing that the first K columns of U can represent the dominant structures of the whole image, we can reduce the training data Tq to TqK, where TqK = UqK ·Tq. In the experiment, we found that K=1 would be the best choice for efficiency. 2. Texture Classification. We use K-means algorithm to realize classification step and thus achieve texture segmentation results. The number of kinds is set as 3 in the experiments.

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