Weighted universal image compression

Weighted universal image compression
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DOI:
10.1109/83.791958
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发表时间:
1999-10
期刊:
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
影响因子:
--
通讯作者:
M. Effros;P. Chou;R. Gray
M. Effros;P. Chou;R. Gray
中科院分区:
其他
文献类型:
--
作者:
M. Effros;P. Chou;R. Gray

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我们描述了一个通用的编码策略,导致一个家庭的通用图像压缩系统的设计,以提供良好的性能在应用程序中的统计数据的源被压缩是不可用的在设计时或随时间或空间而变化。所考虑的基本方法使用一个两阶段的结构,其中传统的图像压缩系统的单个源代码被替换为一个家庭的代码,旨在涵盖一个大类的可能的来源。为了说明这种方法,我们考虑的最佳设计和使用的两个阶段的代码包含集合的矢量量化器(加权通用矢量量化),JPEG风格的编码(加权通用位分配),和变换码(加权通用变换编码)的位分配。此外,我们证明了感知失真措施和最佳解析的列入所获得的好处。该策略产生两阶段的代码,显着优于其单阶段的前辈。在医学图像序列上,加权通用矢量量化优于熵编码矢量量化超过9 dB。在相同的数据序列上,加权通用比特分配优于JPEG风格的代码超过2.5 dB。在混合测试和图像数据的集合上,加权通用变换编码的性能优于单个数据优化变换代码(其性能几乎与JPEG相同)超过6 dB。
We describe a general coding strategy leading to a family of universal image compression systems designed to give good performance in applications where the statistics of the source to be compressed are not available at design time or vary over time or space. The basic approach considered uses a two-stage structure in which the single source code of traditional image compression systems is replaced with a family of codes designed to cover a large class of possible sources. To illustrate this approach, we consider the optimal design and use of two-stage codes containing collections of vector quantizers (weighted universal vector quantization), bit allocations for JPEG-style coding (weighted universal bit allocation), and transform codes (weighted universal transform coding). Further, we demonstrate the benefits to be gained from the inclusion of perceptual distortion measures and optimal parsing. The strategy yields two-stage codes that significantly outperform their single-stage predecessors. On a sequence of medical images, weighted universal vector quantization outperforms entropy coded vector quantization by over 9 dB. On the same data sequence, weighted universal bit allocation outperforms a JPEG-style code by over 2.5 dB. On a collection of mixed test and image data, weighted universal transform coding outperforms a single, data-optimized transform code (which gives performance almost identical to that of JPEG) by over 6 dB.