Abstract

To share the same visual content between color vision defi ciencies (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 diff erent 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.

BibTex:

    @article{hu-2019-colorblind,
        author   = {Xinghong Hu and Xueting Liu and Zhuming Zhang and Menghan Xia and Chengze Li and Tien-Tsin Wong},
        title    = {Colorblind-Shareable Videos by Synthesizing Temporal-Coherent Polynomial Coefficients},
        journal  = {ACM Transactions on Graphics (SIGGRAPH Asia 2019 issue)},
        month    = {November},
        year     = {2019},
        volume   = {38},
        number   = {6},     
        pages    = {174:1-174:12}
    }