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.

翻译：本研究探讨了频繁序列挖掘在有损图像压缩领域的应用范围。所提出的方法基于对像素进行聚类并在压缩过程中使用聚类标识符的思想。该方法将JPEG中的DCT阶段替换为闭合频繁序列挖掘与k-means聚类相结合的方式，以有效处理冗余数据。该方法主要侧重于对图像各分量的所有区块并行应用k-means聚类，从而减少压缩时间。通过新颖的剪枝策略对传统GSP算法进行改进，以优化模式基数，从而实现码表尺寸的有效缩减。对所提算法的仿真实验表明，相较于现有替代方案，其在压缩比与质量方面均取得显著提升。