一种改进的图像表示的增量/递减延迟网格生成策略

Badr Eddine El Marzouki, M. Adams
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引用次数: 0

摘要

提出了两种高效的内容自适应方法来生成数字图像的德劳内网格模型,即HD1和HD2。该方法来源于Adams的增量/递减网格生成框架,采用非平凡初始网格和改进的生长计划。在平方误差和主观质量方面,与竞争方法相比,更高复杂性的IID2方法显示出更高重建质量的网格模型,而较低复杂性的IID1方法以网格质量换取计算成本的降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved incremental/decremental delaunay mesh-generation strategy for image representation
Two highly effective content-adaptive methods for generating Delaunay mesh models of digital images, known as HD1 and HD2, are proposed. The methods are derived from the incremental/decremental mesh-generation framework of Adams by using a non-trivial initial mesh and an improved growth schedule. The higher complexity IID2 method is shown to yield mesh models of superior reconstruction quality compared to competing methods, both in terms of squared error and subjective quality, while the lower complexity IID1 method trades mesh quality in return for a decrease in computational cost.
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