峰值误差约束的最优形状表示

Leu-Shing Lau
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引用次数: 0

摘要

b样条近似是一种有效的形状表示方法。最近,b样条技术也被用于MPEG-4的形状编码。传统的b样条法是一种最小二乘误差(LS)方法,不可避免地会产生一些不理想的峰值误差。为了减轻这种错误,我们安排将极大极小约束纳入设计目标。所得到的方法,称为峰误差约束的最优形状表示(PECOS),是纯LS和纯极大极小设计之间的一种平衡。借助峰值误差约束,可以在相对较低的均方根误差代价下减小峰值误差的大小。例如,峰值误差降低32%的例子很容易获得,代价是均方根误差仅增加3.6% !提出了两种算法来解决PECOS问题。它们都运行得非常快,并且基本上在非常少的迭代(通常低于5次迭代)中收敛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Peak-error-constrained optimal shape representation
B-spline approximation is an efficient tool for shape representation. Recently, the B-spline technique has also been employed for shape coding with regard to MPEG-4. The traditional B-spline method is a least-squared-error (LS) approach which inevitably may bring about certain undesirable peak errors. To alleviate this error, we arrange to incorporate the minimax constraint into the design goal. The resulting method, called peak-error-constrained optimal shape-representation (PECOS), is a balance between the pure LS and pure minimax design. With the aid of the peak-error-constraint, it, is easy to reduce the magnitude of the peak error at a relatively much lower cost of the root-mean-squared (rms) error. For instance, an example of 32% decrease in peak error is easily obtained at the cost of only 3.6% increase of the rms error! Two algorithms are proposed to solve the PECOS problem. Both of them run very fast and basically converge in a very small number of iterations (typically below 5 iterations).
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