Lossy Image Compression-A Comparison Between Wavelet Transform, Principal Component Analysis, K-Means and Autoencoders

For many years, lossless image compression has been a promising topic of study. Various techniques have been created over time to obtain an approximation of the reduced data size. While discrete wavelet transform (DWT) and discrete cosine transform (DCT) have historically been employed for the purpose of compressing images, various machine learning methods and deep learning networks are now being offered. In this research, we conduct a comparative analysis of conventional and contemporary lossy image compression techniques on the Kodak Dataset, including Autoencoders, Principal Component Analysis (PCA), K-Means, and Discrete Wavelet Transform (DWT). The metrics used for the evaluation of the proposed study are Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR), and Structural Similarity Index (SSIM).