用频繁序列挖掘与聚类压缩图像，提升效率与质量。
Lossy Image Compression -- A Frequent Sequence Mining perspective employing efficient Clustering
- 用频繁序列挖掘和k-means聚类替代JPEG的DCT，处理冗余数据。
- 并行处理图像各块，压缩时间显著减少。
- 新剪枝策略降低码表大小，压缩比和质量更优。
本文探索频繁序列挖掘在有损图像压缩中的应用。提出的方法基于像素聚类，用聚类标识符进行压缩。将JPEG中的DCT步骤替换为闭合频繁序列挖掘与k-means聚类的组合，以高效处理冗余数据。通过并行对图像每个分量的所有块执行k-means聚类，显著降低压缩时间。对传统GSP算法进行优化，引入新颖剪枝策略，有效降低模式基数，实现码表规模的显著缩减。仿真结果表明，该方法在压缩比和图像质量方面均优于现有方案。
原文摘要 · Abstract (English)
This work explores the scope of Frequent Sequence Mining in the domain of Lossy Image Compression. The proposed work is based on the idea of clustering pixels and using the cluster identifiers in the compression. The DCT phase in JPEG is replaced with a combination of closed frequent sequence mining and k-means clustering to handle the redundant data effectively. This method focuses mainly on applying k-means clustering in parallel to all blocks of each component of the image to reduce the compression time. Conventional GSP algorithm is refined to optimize the cardinality of patterns through a novel pruning strategy, thus achieving a good reduction in the code table size. Simulations of the proposed algorithm indicate significant gains in compression ratio and quality in relation to the existing alternatives.
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