An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization

Image compression is one of the most important research areas in the field of image processing due to its large number of applications such as aerial surveillance, reconnaissance, medicine and multimedia communication. Even when high data rates are available, image compression is necessary in order to reduce the transmission cost. For applications involving information security, a fast delivery also reduces the chances of compromise over a communication channel. In this paper, we explore the possibility of using one of the computational intelligence techniques, namely, Particle Swarm Optimization (PSO), for optimal thresholding in the 2-D discrete wavelet transform (DWT) of an image. To this end, a set of optimal thresholds is obtained using the PSO algorithm. Finally, a variable length coding scheme, such as arithmetic coding is used to encode the results. Finding an optimal threshold value for the wavelet coefficients is very crucial in reducing the source entropy and bit-rate reduction. The proposed method is tested using several standard images against other popular techniques and proved to be more efficient compared to other methods. •An evolutionary optimization technique is devised for thresholding the wavelet coefficients.•PSO is used to find the optimum thresholds for various sub-bands in the wavelet domain.•The method is adaptive in the sense that a single threshold is not used for all sub-bands.•A VLC coding scheme is used to efficiently compress the wavelet coefficients.