Performance Analysis of Discrete Cosine Transform and Discrete Wavelet Transform Methods on Digital Images Based on Peak Signal-to-Noise Ratio and Mean Squared Error Parameters

Abstract

As the demand for image data storage and distribution continues to increase, compression techniques are required to reduce file size without significantly degrading image quality. One of the most widely used approaches in image compression is frequency-domain transformation, including the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT). This study aims to evaluate and compare the performance of DCT and DWT methods on digital images based on the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) parameters. In addition, the study also considers the compression ratio as a supporting parameter in the evaluation process. The results indicate that the DWT method produced an average MSE value of 63.9381, which was lower than that of DCT method at 70.0780. Meanwhile, DCT method achieved an average PSNR value of 31.21 dB, which was higher than that of DWT method at 30.52 dB. In terms of compression ratio, the DCT method also demonstrated better performance, with an average value of 4.2954, whereas DWT method produced an average value of 2.0275. Based on the testing results, it can be concluded that DWT method performs better in minimizing image distortion based on the MSE parameter, whereas the DCT method is superior in producing reconstructed image quality and compression efficiency based on PSNR values and compression ratio.