TMM Volume 21 Issue 12

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December, 2020

TMM Volume 21 Issue 12

Generating images via a generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolution images of limited quality. Directly generating high-resolution images using GANs is nontrivial, and often produces problematic images with incomplete objects.

The scalable video coding extensions of the High Efficient Video Coding (HEVC) standard (SHVC) have adopted a new quadtree-structured coding unit (CU). The SHVC test model (SHM) needs to test seven intermode sizes and one intramode size at depth levels of “0,” “1,” “2,” and four intermode sizes and two intramode sizes at a depth level of “3” for interframe CUs.

Using deep convolutional neural networks (CNN) to predict the depth from a single image has received considerable attention in recent years due to its impressive performance. However, existing methods process each single image independently without leveraging the multiview information of video sequences in practical scenarios.

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