Automatic compression for image sets using a graph theoretical framework
Automatic compression for image sets using a graph theoretical framework
复制标题
使用图论框架自动压缩图像集
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
B. Gergel
中科院分区：
文献类型：
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作者：
B. Gergel
本文提出了一种新的适用于任意图像集的自动压缩方案。该方案不需要先验知识的图像集的属性。该方案是使用一个统一的图形理论框架，允许压缩策略进行比较，理论和实验。该策略通过计算从图像集构造的图的最小生成树来实现最佳无损压缩。对于有损压缩，该方案是接近最优的，并且提供了相对于最优方案的性能保证。实验结果表明，这种压缩策略相比，毫不逊色于先前提出的策略，与无损压缩的情况下，72%的有损压缩的情况下，提高了7%。本论文还表明，底层压缩算法的选择是重要的压缩图像集使用所提出的计划。
A new automatic compression scheme that adapts to any image set is presented in this thesis. The proposed scheme requires no a priori knowledge on the properties of the image set. This scheme is obtained using a unified graph-theoretical framework that allows for compression strategies to be compared both theoretically and experimentally. This strategy achieves optimal lossless compression by computing a minimum spanning tree of a graph constructed from the image set. For lossy compression, this scheme is near-optimal and a performance guarantee relative to the optimal one is provided. Experimental results demonstrate that this compression strategy compares favorably to the previously proposed strategies, with improvements up to 7% in the case of lossless compression and 72% in the case of lossy compression. This thesis also shows that the choice of underlying compression algorithm is important for compressing image sets using the proposed scheme.