Image Compression Using FFT, DCT, HT, Wavelet Transforms and Vector Quantization Techniques

This project presents a MATLAB implementation of image compression utilizing advanced transform and coding techniques including Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Hadamard Transform (HT), Wavelet Transform, and Vector Quantization. These techniques enable efficient transformation and compression of image data to reduce storage space and transmission bandwidth requirements. The MATLAB implementation involves key functions such as fft2() for frequency domain conversion, dct2() for block-based compression, wavelet families like 'db4' for multi-resolution analysis, and vector quantization algorithms for codebook generation. The system performs transform domain processing followed by coefficient thresholding and quantization to achieve significant compression ratios while maintaining acceptable image quality. Through this implementation, we demonstrate how these mathematical transforms can effectively reduce image file sizes while preserving visual quality, thereby improving transmission speed and storage efficiency. The code includes performance metrics such as PSNR calculation and compression ratio analysis to evaluate the effectiveness of each transformation method.