Fast weighted universal transform coding: toward optimal, low complexity bases for image compression

M. Effros
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引用次数: 2

Abstract

Effros and Chou (see Proceedings of the IEEE International Conference on Image Processing, Washington, DC, 1995) introduce a two-stage universal transform code called the weighted universal transform code (WUTC). By replacing JPEG's single, non-optimal transform code with a collection of optimal transform codes, the WUTC achieves significant performance gains over JPEG. The computational and storage costs of that performance gain are effectively the computation and storage required to operate and store a collection of transform codes rather than a single transform code. We consider two complexity- and storage-constrained variations of the WUTC. The complexity and storage of the algorithm are controlled by constraining the order of the bases. In the first algorithm, called the fast WUTC (FWUTC), complexity is controlled by controlling the maximum order of each transform. On a sequence of combined text and gray-scale images, the FWUTC achieves performance comparable to the WUTC. In the second algorithm, called the jointly optimized fast WUTC (JWUTC), the complexity is controlled by controlling the average order of the transforms. On the same data set and for the same complexity, the performance of the JWUTC always exceeds the performance of the FWUTC. The JWUTC and FWUTC algorithm are interesting both for their complexity and storage savings in data compression and for the insights that they lend into the choice of appropriate fixed- and variable-order bases for image representation.
快速加权通用变换编码:面向图像压缩的最优、低复杂度基础
Effros和Chou(参见IEEE国际图像处理会议论文集,华盛顿特区,1995)介绍了一种称为加权通用变换码(WUTC)的两阶段通用变换码。通过用一组最优转换代码替换JPEG的单个非最优转换代码,WUTC比JPEG获得了显著的性能提升。这种性能增益的计算和存储成本实际上是操作和存储一组转换代码而不是单个转换代码所需的计算和存储成本。我们考虑WUTC的两种复杂性和存储约束的变体。通过约束基的顺序来控制算法的复杂度和存储量。在第一种算法中,称为快速WUTC (FWUTC),通过控制每个变换的最大阶数来控制复杂度。在组合文本和灰度图像的序列上,FWUTC实现了与WUTC相当的性能。第二种算法称为联合优化快速WUTC (JWUTC),通过控制变换的平均阶数来控制复杂度。对于相同的数据集和相同的复杂度,JWUTC的性能总是优于FWUTC的性能。JWUTC和FWUTC算法的有趣之处在于它们在数据压缩方面的复杂性和存储节省,以及它们为图像表示选择适当的固定顺序和可变顺序基所提供的见解。
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