Compression of High-Spatial-Resolution Images Based on Estimating the Detail Sub-bands in the Wavelet Domain

Proper spatial resolution has great importance in many image types since it conveys significant details. Performance of feature extraction methods, in some image types such as textual, facial, and fingerprint ones, highly depend on the image quality. Spatial resolution is one of important factors affecting the image quality; but, high spatial resolution increases the storage memory of the corresponding images, showing the importance of image compression methods.
In the proposed image compression approach of this paper,dimension of the input image is first decreased based on the wavelet transform and then is compressed using any desired image compression method. In the decompression stage, first, the dimensionally reduced image is reconstructed and then, the initial dimension is restored by our proposed technique of estimating the detail sub-bands in the wavelet domain.
In the evaluation stage, we chose two image types of textual and facial as two case studies having band-pass and low-pass spectrums respectively. We evaluated the compression and recognition performance of proposed approach by combining it with any of conventional compression methods of JPEG, JPEG2000 and SPIHT.Simulation results showed the noticeable effect of the proposed approach on reducing the storage memory and simultaneously, preserving o the compression/recognition performance.