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Statistical split and polynomial merge algorithm for image representation

机译:统计拆分和多项式合并算法的图像表示

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Abstract: Since polynomials fit the geometrical forms of images harmoniously and well represent slowly varying surfaces in images, there were many split and merge algorithms which used a polynomial function to represent each homogeneous region. Even though very low-bit rate can be achieved using their algorithms, it takes too much time for both split process and merging process. Furthermore, the splitted result is not quite well matched to HVS, either. In this paper, a new split and merge algorithm is designed. In this algorithm the split process uses a statistical hypothesis test called ShortCut method as a measurement of region homogeneity, and the merge process uses a polynomial function. The computation time for the split process can be significantly reduced using the new algorithm, and the new scheme reflects HVS more than any other scheme. To justify the algorithm proposed here, it is compared with other algorithms including Kunt's algorithm.!14
机译:摘要:由于多项式和谐地拟合图像的几何形式,并且很好地表示图像中变化缓慢的表面,因此存在许多使用多项式函数表示每个均匀区域的拆分和合并算法。即使使用他们的算法可以实现非常低的比特率,但拆分过程和合并过程都需要花费太多时间。此外,分割结果也与HVS不太匹配。本文设计了一种新的拆分合并算法。在该算法中,拆分过程使用称为ShortCut方法的统计假设检验作为区域均匀性的度量,合并过程使用多项式函数。使用新算法可以显着减少拆分过程的计算时间,并且新方案比其他任何方案都更能体现HVS。为了证明此处提出的算法的合理性,将其与包括肯特算法在内的其他算法进行了比较!14

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