Lossy Image Compression Unveiled: A Comprehensive Evaluation of DCT, Wavelet Transform, and Vector Quantization

The increasing demand for efficient image storage and transmission has driven extensive research into lossy image compression algorithms. This paper presents a comprehensive comparative analysis of three prominent lossy image compression techniques: Discrete Cosine Transform (DCT), Wavelet Transform, and Vector Quantization (VQ). Employing a diverse dataset and assessing their performance through key metrics, including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), Bitrate, and Computational Complexity, we meticulously evaluated these techniques across dimensions of image quality, compression efficiency, and computational demands.DCT emerges as a standout performer in preserving image quality, closely followed by Wavelet Transform. While Vector Quantization demonstrates efficiency in compression, its limitations become apparent in the realm of image quality preservation. The comparative analysis unequivocally positions DCT as the optimal choice for applications prioritizing image quality. This preference is substantiated by its remarkable PSNR and SSIM scores. Despite DCT not being the most computationally efficient, its ability to strike a crucial balance between compression efficiency and image quality renders it a well-rounded and effective solution.In conclusion, this research provides valuable insights into the comparative performance of DCT, Wavelet Transform, and VQ in the context of lossy image compression. The findings underscore DCT's superiority in image quality preservation, offering practical guidance for decision-makers in the field. The paper contributes to informed choices based on specific application requirements and emphasizes the pivotal role of DCT as a well-rounded and effective solution.