Compression and Denoising - Comparative Analysis from still Images using Wavelet Techniques

With the growth of the multimedia technology over the past decades, the demand for digital information has increased dramatically. This enormous demand poses difficulties for the current technology to handle. One approach to overcome this problem is to compress the information by removing the redundancies present in it. This is the lossy compression scheme that is often used to compress information such as digital images. The main objective is to investigate the still image format compression and de-noising using different wavelet techniques. The “Compression and Denoising – Comparative Analysis from still Images using Wavelet Techniques” is implemented in software using „MATLAB2012a‟ version Wavelet Toolbox and 2-D DWT technique. The purpose is to analyze still images using different wavelets families such as Haar, Daubechies, Coiflets, Symlets, Discrete Meyer, Biorthogonal and Reverse Biorthogonal. The experiments and simulation is carried out on still image .jpg formats. This work tries to introduce wavelets and then some of its applications and technique in image processing. The scope of the work involves– Compression and de-noising, image clarity and comparing the results of wavelet families, to find the effect of the decomposition and threshold levels and to find out energy retained (image recovery) and lost, knowing the best wavelet and so on. The wavelet differs from each other in image clarity and energy retaining. Each method is compared and classified in terms of its efficiency at different decomposition and threshold levels. Therefore, the image recovery is good and clarity, but the percentage of compression and retaining the energy is different. In order to quantify the performance of the de-noising, a noise is added to the still image and given as input to the de-noising algorithm, which produces an image close to the original image.