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Distortion-aware image retargeting based on continuous seam carving model

机译:基于连续缝雕刻模型的畸变图像重定向

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The seam-carving algorithm is a classic context-aware image retargeting method and has been extensively studied for years; however, distortions exist as the discrete iteration of least- energy computation. We propose a continuous seam carving model through the just noticeable distortion (JND) detection at every iteration and accumulative energy weight. The JND of every pixel is calculated by the minimal just-noticeable distortion energy of adjacent-pixel conflicting displacements between seam carving iterations. The mean field approximation is used to efficiently solve the problem. In this way, the proposed energy weight can accumulatively calculate the JND information of recent k iterations and passed down to the next iteration for distortion avoidance. The superior performance of the proposed continuous seam carving model, as compared with state-of-art image retargeting approaches in RetargetMe database, was demonstrated experimentally. (C) 2019 Published by Elsevier B.V.
机译:拼缝算法是一种经典的上下文感知图像重定位方法,并且已经进行了多年的深入研究。但是,失真是最小能量计算的离散迭代。我们通过每次迭代和累积能量权重下的可察觉的失真(JND)检测,提出了一个连续的接缝雕刻模型。每个像素的JND由接缝雕刻迭代之间相邻像素冲突位移的最小正好可察觉的失真能量来计算。平均场近似用于有效解决问题。以此方式,所提出的能量权重可以累积地计算最近k次迭代的JND信息,并向下传递至下一次迭代,以避免失真。与RetargetMe数据库中最新的图像重定向方法相比,该实验证明了所提出的连续缝雕刻模型的优越性能。 (C)2019由Elsevier B.V.发布

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