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Perceptually meaningful quadtree decomposition using dual-homogeneous criteria

机译:使用双重齐次准则的感知上有意义的四叉树分解

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Saliency detection is a powerful tool for many applications because it can provide valuable information reflecting human visual attention. Quadtree decomposition is a classical and efficient image operator which can divide image into square blocks in different scales. The blocks in smaller size contain valuable information such as edges and texture. In image lossy compression or similar applications, we want to reserve valuable regions (salient objects) as much as we can, meanwhile, we also want to suppress non-salient regions human are not interested in. For these reasons, we propose an ingenious saliency guided quadtree decomposition model. The proposed model outperforms traditional methods in two aspects: It reserves the salient regions well, and the edges can be protected simultaneously.
机译:显着性检测是许多应用程序的强大工具,因为它可以提供反映人类视觉注意力的有价值的信息。四叉树分解是一种经典而有效的图像运算符,可以将图像分成不同比例的正方形块。较小尺寸的块包含有价值的信息,例如边缘和纹理。在图像有损压缩或类似的应用程序中,我们希望尽可能多地保留有价值的区域(显着对象),同时,我们也希望抑制人类不感兴趣的非显着区域。出于这些原因,我们提出了一种巧妙的显着性制导的四叉树分解模型。所提出的模型在两个方面都优于传统方法:保留了突出的区域,并且可以同时保护边缘。

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