Colorblind-shareable videos by synthesizing temporal-coherent polynomial coefficients
Colorblind-shareable videos by synthesizing temporal-coherent polynomial coefficients
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DOI：
10.1145/3355089.3356534
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Xinghong Hu;Xueting Liu;Zhuming Zhang;Menghan Xia;Chengze Li;Tien-Tsin Wong
中科院分区：
文献类型：
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作者：
Xinghong Hu;Xueting Liu;Zhuming Zhang;Menghan Xia;Chengze Li;Tien-Tsin Wong
为了在色觉缺陷（CVD）和正常视力的人之间共享相同的视觉内容，已经尝试将双目显示器的两种视觉体验（戴眼镜和不戴眼镜）分配给CVD和正常视力的观众。然而，现有的方法仅适用于静止图像。虽然可以应用最先进的时间滤波技术来平滑每帧生成的内容，但它们可能无法保持我们的应用中所需的多个双目约束，甚至更糟的是，有时会引入颜色不一致（相同的颜色区域映射到不同的颜色）。在本文中，我们提出训练一个神经网络来预测全局颜色分解域中的时间相干多项式系数。这种间接公式化解决了颜色不一致的问题。我们的关键挑战是设计一个神经网络来预测时间相干系数，同时保持所有必需的双目约束。我们的方法在各种视频上进行了评估，所有指标都证实它优于所有现有的解决方案。
To share the same visual content between color vision deficiencies (CVD) and normal-vision people, attempts have been made to allocate the two visual experiences of a binocular display (wearing and not wearing glasses) to CVD and normal-vision audiences. However, existing approaches only work for still images. Although state-of-the-art temporal filtering techniques can be applied to smooth the per-frame generated content, they may fail to maintain the multiple binocular constraints needed in our applications, and even worse, sometimes introduce color inconsistency (same color regions map to different colors). In this paper, we propose to train a neural network to predict the temporal coherent polynomial coefficients in the domain of global color decomposition. This indirect formulation solves the color inconsistency problem. Our key challenge is to design a neural network to predict the temporal coherent coefficients, while maintaining all required binocular constraints. Our method is evaluated on various videos and all metrics confirm that it outperforms all existing solutions.