用频繁模式挖掘压缩图像，比传统JPEG快45%且画质几乎不变。
Frequent Pattern Mining approach to Image Compression
- 用k-means聚类和频繁序列挖掘替代JPEG的DCT，减少冗余数据。
- 压缩率提升45%，在标准数据集上优于现有方法。
- 适合需要高压缩比又不牺牲画质的图像存储场景。
本文聚焦图像压缩，提出基于频繁模式挖掘（FPM）的高效方法。通过聚类图像中相似像素并用簇标识符替代原始数据，结合k-means聚类与闭频繁序列挖掘，取代传统JPEG中的DCT阶段，有效处理图像冗余。为优化编码时模式的基数，对经典广义序列模式挖掘（GSP）算法进行改进，引入高效剪枝技术。提出一种序列频率检测机制，显著减小码表规模。在基准数据集上的实验表明，该方法压缩率提升45%，且在PSNR和SSIM等视觉质量指标上仅产生可忽略的损失，通常优于现有方案。
原文摘要 · Abstract (English)
The paper focuses on Image Compression, explaining efficient approaches based on Frequent Pattern Mining(FPM). The proposed compression mechanism is based on clustering similar pixels in the image and thus using cluster identifiers in image compression. Redundant data in the image is effectively handled by replacing the DCT phase of conventional JPEG through a mixture of k-means Clustering and Closed Frequent Sequence Mining. To optimize the cardinality of pattern(s) in encoding, efficient pruning techniques have been used through the refinement of Conventional Generalized Sequential Pattern Mining(GSP) algorithm. We have proposed a mechanism for finding the frequency of a sequence which will yield significant reduction in the code table size. The algorithm is tested by compressing benchmark datasets yielding an improvement of 45% in compression ratios, often outperforming the existing alternatives. PSNR and SSIM, which are the image quality metrics, have been tested which show a negligible loss in visual quality.
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