Frequent Pattern Mining approach to Image Compression

TL;DR

This paper introduces a novel image compression method using Frequent Pattern Mining that clusters similar pixels and replaces traditional DCT with clustering and sequence mining, achieving significant compression improvements.

Contribution

It proposes a new image compression technique combining clustering and sequence mining, outperforming existing methods with a 45% improvement in compression ratios.

Findings

45% improvement in compression ratios

Negligible loss in image quality metrics

Outperforms existing compression alternatives

Abstract

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.