Quantization based wavelet transformation technique for digital image compression with removal of multiple artifacts and noises

A Morlet’s wavelet transformation based image compression and decompression (MWT-ICD) technique is proposed in order to enhance the performance of digital and gray scale image compression with higher compression ratio (CR) and to reduce the space complexity. The MWT-ICD technique initially performs preprocessing task to remove multiple artifacts and noises in digital and gray scale images with the application of generalized lapped orthogonal transforms and Wiener filter. This process results in improved quality of digital and gray scale images with higher PSNR for compression. Next, wavelet quantization transformation based image compression algorithm is developed in MWT-ICD Technique using Morlet’s wavelet transformation. Finally, quantized wavelet transformation based image decompression process is carried out in MWT-ICD technique with the objective of obtaining the reconstructed original image. The performance of MWT-ICD technique is measured in terms of CR, compression time (CT), and space complexity (SC), peak signal to noise ratio (PSNR) and compared with four existing methods. The experimental results show that the MWT-ICD technique is able to acquire higher CR and also reduced space complexity when compared to existing DCT-based image compression system based on Laplacian transparent composite model and multi-wavelet based compressed sensing technique