Optimal quantization table generation for efficient satellite image compression using teaching learning based optimization technique

Abstract

Communication of the satellite images plays a crucial role in many important applications such as change detection, land cover classification, weather prediction etc. The increase in the demand of satellite images over the band limited channel requires good quality compression tools. Currently, the compression standard developed by Joint Photographic Experts Group (JPEG) is the widely used product for the compression of satellite images. Moreover, one of the important elements which influence the performance of JPEG compression standard is the nature of quantization table. The compression ratio and the decompressed image quality are determined all-together by the quantization table, and hence, the table strongly influences the whole compression performance. The author aims to generate better quantization tables to enhance the compression performance to achieve higher compression ratios while preserving high reconstruction quality for satellite images. A Teaching Learning based Optimization (TLBO) technique is employed to promote higher compression performance for satellite images. The aim is to identify optimal quantization tables that contribute to better compression efficiency in terms of image quality indexes Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The TLBO based optimization strategy is proposed because it is free from the algorithmic parameters. Experiments were carried out on the three different satellite images. The results demonstrate that the identified optimal quantization tables offer average PSNR gain of about 0.5 dB with the average reduction in MSE of about 10 against the available JPEG quantization table.