Enhancing Lossy Image Compression Using Wavelet Coding with a Scale-Level Skip Model

To improve compression of data effectiveness, this study introduces a new Scale Level Skip Model for wavelet coding, which tries to decrease redundancies in the wavelet decomposition process. Repeated evaluation of repetitive wavelengths and components causes computing overhead, which is a common difficulty for conventional wavelet coding schemes. The suggested technique presents an adaptable band filtering method that finds and removes unnecessary wavelet sub-bands by means of inter- and intra-correlation analysis. This chosen method keeps crucial low- and high-frequency details for efficient reconstructing while reducing scans and processing expenditure. For improved frequencies localisation and finer resolutions, the algorithm uses discrete wavelet transform (DWT) with multi-wavelet decomposing. By separating and removing inconsequential parameters, a filtration approach that utilises mean square deviation (MSD) improves the reduction process significantly. Using very large-scale integration (VLSI) technology, the design guarantees optimal hardware realisation and enables faster, more accurate applications in real time. The experimental findings show that the suggested model outperforms the existing methods in relation to compression ratio, PSNR, and computing efficiency. For high-quality imaging applications in situations with limited resources, the Scale Level Skip Model is the way to go because it drastically reduces processing delay and energy usage and its accuracy is 99% compared to other models.