Integration of Discrete Wavelet Transform (DWT) Technology

This study integrates Discrete Wavelet Transform (DWT) technology and leverages the complementary characteristics of DWT and HVS (Human Visual System) to develop a new objective image quality assessment methodology. The approach utilizes DWT's multi-scale analysis capabilities through wavelet decomposition (implemented using functions like wavedec2 in MATLAB), which effectively captures detailed texture information across different frequency bands. By incorporating HVS perceptual mechanisms - including contrast sensitivity and masking effects modeled through psychovisual thresholds - the method achieves enhanced accuracy and reliability in quality evaluation. Key implementation steps involve: 1) Multi-level DWT decomposition using Daubechies or biorthogonal wavelets 2) Perceptual weighting of subband coefficients based on HVS models 3) Statistical feature extraction from wavelet coefficients 4) Quality score computation through feature aggregation. This research contributes significantly to advancing image quality assessment standards and image processing techniques, particularly for applications in compression optimization and visual communication systems.