Edge Preserved Image Compression using Extended Shearlet transform

Corresponding Author: Thayammal, S. Department of ECE Dhanalakshmi College of Engineering, Chennai-601 301, Tamil Nadu, India Email: [email protected] Abstract: The motivation of the proposed compression method is to reduce the bit rate for image transmission or memory requirement for image storage while maintaining image quality. The edges are one of the prominent features in an image and they are essential for maintaining image quality. JPEG compression standards like JPEG98 and JPEG2000 produce visual artifacts in reconstructed image at low bit rate because, they didn’t tailored about the detailed information like edges. Hence second generation coding introduced to preserve edge information, in which approximation and detailed information are separately encoded, so that, it introduces additional computational time and complexity. A multi directional anisotropic shearlet transform provides an optimally efficient representation of images with edges whereas wavelet transform have limited capability in dealing with edge information in all directions. Here, multidirectional transform called extended shearlet transform is used to uncorrelate the input gray level values with edge preserving capabiltiy. Hard thresholding method is applied to transform coefficients and finally threshold output is encoded using Set Partitioning In Hierarchical Trees (SPIHT) technique. The comparative analysis is performed between Edge Preserved Wavelet Transform coding (EPWT) and the extended shearlet transform coding. Image quality is measured objectively using peak signal-to-noise ratio, Structural Similarity Index (SSIM) and subjectively, using perceived image quality. The simulation results show that, the extended shearlet based compression technique is more efficient than EPWT coding technique for wide range of geometrical features of the images. Quantitative analysis on standard test images show that the proposed technique outperforms the EPWT coding technique by 0.16 to 1.46 dB of PSNR with less computational time.