In the research field of infrared imaging techniques, there is an important and popular technique that has enormous data to be stored and transmitted. Various algorithms have been proposed to improve the performance of the compression scheme. In this paper we extended a traditional wavelet algorithm to an image compression with updated technology and then compare its performance with others based on normal parameters. It is well known that lifting based Cohen-Daubechies-Feauveau wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform is very efficient and effective in image compression which coupled with Set Partition in Hierarchical Trees (SPIHT) coding algorithm and entropy coding techniques. One of our contributions in this paper is to demonstrate the choice of decomposition level is playing a very important role in achieving superior wavelet compression performances. Image quality is assessed objectively by parameters of compression ratio, peak signal-to-noise ratio (PSNR), mean structural similarity index (MSSIM). It is also evaluated subjectively by using perceived image quality. It needs to be highlighted that comparative the compression ratio has been significantly improved by 88%, together with highest PSNR values and MSSIM is close to 1 and found the best decomposition level and required bit rate per pixel for infrared (IR) images with our algorithm.
|Number of pages||8|
|Journal||International Journal of Computer Science and Network Security|
|Publication status||Published - 2012|