Improved image quality measure through particle swarm optimization

In this paper we present new approach to the objective image quality evaluation based on discrete wavelet transform (DWT) and particle swarm optimization (PSO). DWT was applied on image difference (difference between original and degraded image) that is decomposed into approximation and detail subbands. DWT coefficients were computed using Coiflet wavelet filter banks. The coefficients were used to compute new image quality measure (IQM) that is defined as perceptual weighted difference between coefficients of original and degraded image. Weighting factors for wavelet subbands have been experimentally determined using PSO optimization algorithm to achieve the best possible correlation with results of subjective (perceptual) image quality evaluation. Experimental results demonstrate that the proposed technique has high correlation with results of subjective test and low computational time important for real-time applications.