IMPLEMENTATION OF DCT (DISCRETE COSINE TRANSFORM) AND MEDIAN CUT ALGORITHMS FOR CONVERTING JPG IMAGE FILES INTO GIF FORMAT

Abstract

Digital image compression plays an important role in reducing storage requirements and improving transmission efficiency while maintaining acceptable visual quality. This study implements the Discrete Cosine Transform (DCT) and Median Cut algorithms to convert JPG images into GIF format through a desktop-based application developed using Python. The proposed method applies DCT to transform image data from the spatial domain into the frequency domain, enabling the reduction of high-frequency coefficients that contribute less to visual perception. The reconstructed image is subsequently processed using the Median Cut algorithm for color quantization to generate an optimized color palette suitable for the GIF format. The application was developed with a graphical user interface (GUI) using Tkinter to facilitate image selection, parameter configuration, processing visualization, and performance evaluation. Experimental testing was conducted using multiple color palette configurations (256, 128, 64, 32, and 8 colors). Performance was evaluated based on file size, compression ratio, reduction size, mean squared error (MSE), and peak signal-to-noise ratio (PSNR). The results demonstrate that the proposed approach effectively reduces image file size while preserving acceptable visual quality. A smaller color palette produces higher compression efficiency but increases reconstruction error, resulting in higher MSE and lower PSNR values. Overall, the combination of DCT and Median Cut provides an effective approach for JPG-to-GIF image conversion by achieving a balance between compression efficiency and image quality.

Keywords : Discrete Cosine Transform, DCT, Median Cut, Image Compression, Color Quantization, GIF, JPG, Python, Digital Image Processing