29,753 research outputs found

    On color-to-gray transformation for distributing color digital images

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    A new block embedding algorithm with a secret key is introduced for the Color-to-Gray and Back transformation. Color is embedded into the bit planes of the luminosity component of the YUV color space. We present an interface incorporated into Adobe Photoshop and consider a solution of the high quality digital image distribution problem. The solution is based on idea that the grayscale image with hidden color is a β version of a color original. Then a user could get a gray image before buying its color original he is interested. The proposed protocol has a secret key and can protect original images from unauthorized copying

    Pixelated Semantic Colorization

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    While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed convolutional neural network includes two branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more realistic and finer results compared to the colorization state-of-the-art
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