【SIGGRAPH Asia 2012专题*技术论文】图像操作：图像完成质量预测

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【SIGGRAPH Asia 2012专题*技术论文】

图像操作：图像完成质量预测

Quality Prediction for Image Completion

Johannes Kopf, Wolf Kienzle, Steven ***cker, Sing Bing Kang



















摘要：我们提出一个数据驱动的方法来预测图像质量的完井方法。我们的方法是基于最先进的非参数框架韦克斯勒等人。2007。它采用自动搜索空间的限制补丁的源区，导致改进的纹理合成和语义上更合理的结果。这些限制也方便性能预测使我们能够相互关联的输出质量特性可能的地区用于合成。我们利用我们的算法fi第一作物和完成缝合全景。我们的预测能力是用来fi和最佳产量形状之前完成的计算，节省了计算量不显著fi。我们的优化作物包括了原来的全景尽可能避免区域，可以使在不太成功fi。我们的预测，也可以用于孔fi落在内部的图像。除了广泛的比较结果，我们进行了用户研究验证我们的预测功能，良好的相对质量的结果对其他国家最先进的算法，和我们的自动裁剪算法。



Abstract：We present a data-driven method to predict the quality of an image completion method. Our method is based on the state-of-the-art non-parametric framework of Wexler et al. [2007]. It uses automatically derived search space constraints for patch source regions,which lead to improved texture synthesis and semantically more plausible results. These constraints also facilitate performance prediction by allowing us to correlate output quality against features

of possible regions used for synthesis. We use our algorithm to first crop and then complete stitched panoramas. Our predictive ability is used to find an optimal crop shape before the completion is computed, potentially saving significant amounts of computation. Our optimized crop includes as much of the original panorama as possible while avoiding regions that can be less successfully filled in.Our predictor can also be applied for hole filling in the interior of images. In addition to extensive comparative results, we ran several user studies validating our predictive feature, good relative quality of our results against those of other state-of-the-art algorithms, andour automatic cropping algorithm.



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