Improved Compression of DMSAR data using LBG-Algorithm based Vector Quantizer

In this paper, we demonstrate the superiority of Block Adaptive Vector Quantization (BAVQ) over conventional scalar Block Adaptive Quantization (BAQ) for compressing the on-board uncompressed raw data of ISRO’s Disaster Management Synthetic Aperture Radar (DMSAR). For detailed analysis, we also simulate the input Gaussian data covering wide range of Sigma below Clip Levels (SBCLs) and show its effective compression with the help of unique optimum codebooks via BAVQ technique. We illustrate that BAVQ outperforms BAQ in compressing simulated as well as DMSAR data by achieving improved Signal to Quantization Noise Ratio (SQNR) for given compression ratio.