Lossy Image Compression -- A Frequent Sequence Mining perspective employing efficient Clustering

TL;DR

This paper introduces a novel lossy image compression method that combines frequent sequence mining with clustering to improve compression ratio and quality, replacing traditional DCT in JPEG.

Contribution

It presents a new approach integrating frequent sequence mining and k-means clustering for efficient lossy image compression, optimizing pattern cardinality and reducing code table size.

Findings

Significant gains in compression ratio.

Improved image quality compared to existing methods.

Reduced compression time through parallel clustering.

Abstract

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.