Image Compression Exploration using Discrete Wavelets Transform Families and Level

This analysis paper is based on Discrete Wavelets Transform (DWT) for image compression using wavelets families and levels. The DW transforms the image or data into frequency components that match its resolution scale while the compression removes duplication and unwanted information on the receiver side. Wavelets in compression observe the whole image very finely and thus produce no blocking artifacts. Thus, wavelets are high-quality image compression used in many real-world applications i.e. image, multimedia, biometric and biological analysis, computer graphics and image processing, etc. In this investigation, first of all, various compression methods have been compared. It is validated based on compression ratio thatDWT is the optimal choice. Secondly, for experimental and analysis purposes, random real-time digital images both RGB and greyscale havebeen used as a dataset. The assessment images have been converted to grayscale if RGB, decomposed using wavelet levels, and compressed using wavelet families. Threshold coefficients have been evaluated by the Birge-Massart strategy using two scenarios i.e. simulator control thresholding and increasing threshold. Birge-Massart thresholding is best for the compression of still images in wavelet transform. The evaluation and comparison of various wavelet families and decomposition levels were conducted based on criteria such as image compression effectiveness, retained energy, and zero coefficients. The size of original, compressed, and decompressed images has also been computed and displayed for analysis purposes. The analysis of wavelet families and decomposition levels indicated that increasing levels up to a certain range for decomposition purposes in variouswavelet compression families enhances image smoothness consistently. With image smoothness, roughness, and noise spikes in images have been reduced. However, it is observed that after specific levels, image quality degradation has been observed. The significance and novelty of the work provide analysis for appropriate and effective quality image compression using DWT families and levels in different applications. The purpose is to reduce need-based storage requirements and lightweight transmission. Additionally, the optimum compression algorithm in DWT families and levels is also found based on the results. As selection of wavelet filters and decomposition level play an important role in achieving an effective compression performance because no filter performs the best for all images.