Image Compression Scheme based on Optimized K-means Clustering and Higher-Level Decomposed DWT

Abstract

Image processing is a vital research domain essential in day-to-day digital activities. Image compression reduces memory space, transmission bandwidth requirement, transmission time, and computational complexity. This article aims to construct and compare hybrid image compression schemes using DWT, PCA, and K-means clustering techniques. This research proposes an image compression scheme based on optimized K-means clustering and higher-level decomposed DWT (K-DWT). Also, we have developed the hybrid image compression schemes based on PCA and DWT (P-DWT), PCA, K-means, and DWT (P-K-DWT) and analyzed the performance. Initially, we implemented compression algorithms using K-means Clustering, PCA, and DWT. Further, using the python platform, K-DWT, P-DWT, and P-K-DWT compression models have been executed on the images of the kodak dataset. The research objective is to obtain good image quality and better compression algorithms. The image quality and compression efficiency are evaluated using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and Compression Ratio (CR). The K-DWT methods have achieved a higher compression ratio, i.e., 15.3870, compared with other methods. Also, the outcomes show that the K-DWT method has provided the required values of PSNR and SSIM.