Study of EZW compression techniques for high and low resolution satellite imageries

Image compression methods employing wavelet transforms have been successfully implemented to provide high compression rates while maintaining good image quality. The main contribution of wavelet theory and multi-resolution analysis is that, it provides an elegant framework in which both anomalies (such as edges and object boundaries) and trends (areas of high statistical spatial correlation) can be analyzed on an equal footing This results in a considerable improvement in encoding the significance map, and hence, a higher efficiency in compression. By employing the Successive approximation entropy coded quantization (SAQ), the EZW (Embedded Zero-tree Wavelet) coder generates a representation of the image that is coarser-to-finer in both the spatial domain and frequency domain simultaneously. Applying the DWT coefficients and EZW techniques using five threshold values, the maximum compression ratios achieved for an acceptable quality of the image have derived. The three types of images LENA Image and high resolution urban image (SatUImg and low resolution Rural (SatRImg) image have been considered for the analysis. The results show that for the standard Lena image one can achieve highest compression ratios (~11.28). where as the Rural Images (SatRImg) shows better compression ratio (~5.75) compared to that of Urban Images (satUImg) which is small (~1.39). The results are presented and discussed in the paper.