Optimization of 2D-Wavelet Filters Based on Taylor Hybrid BAT Algorithm

The aim of image compression is to minimize the redundancy of the image data in order to process or transfer the images quickly. It also decreases the file size and allows small space for additional files to be stored. In lossy wavelet based image compression, it is not possible to fully recover the input image, since the quantization error effect is greater. But it can be minimized by using an optimized filter bank. The regular wavelet filter coefficients and its inverse filter were optimized in this proposed novel Taylor hybrid bat Algorithm. The optimized wavelet filters are utilized by SPIHT encoder/decoder for picture compression. Performance metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measures (SSIM) are utilized in image restoration during the decompression process for performance of optimized filters. The obtained results show that by restricting the errors between the input and the decompressed image, the proposed filters beat the traditional wavelet filters.