Image Compression Using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT)

In this article, we explore an image compression technique called "Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT)" - a method for reducing image size and minimizing storage requirements. Both DWT and DCT are widely used image compression techniques that decompose images into frequency components, then reduce image size by discarding certain frequency elements. The primary distinction between DWT and DCT lies in their basis functions: DWT utilizes wavelet functions while DCT employs cosine functions as foundation elements. In implementation, DCT algorithms typically use block-based processing (often 8x8 pixel blocks) with functions like dct2() in MATLAB, while DWT employs multi-resolution analysis through wavelet filters like 'haar' or 'db4'. Both techniques have distinct advantages and limitations, but they remain highly effective image compression methods that can significantly reduce image size while preserving acceptable image quality through proper thresholding and quantization strategies applied to the transformed coefficients.