A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Abstract

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques. K E Y W O R D Scontextual compression, contourlet transform, magnetic resonance imaging brain image, optimized prediction, wavelet transform Int.

An efficient and high quality medical CT image enhancement algorithm

“…There are also many algorithms for image enhancement in the transform domain such as and wavelet transform. Wavelet transform has been widely used in image processing because of its excellent time‐frequency localization and multi‐resolution analysis capabilities 20–22 . A sharpened medical image using a stationary wavelet transform to enhance illumination was proposed in Reference 20 .…”

A combined HMM–PCNN model in the contourlet domain for image data compression

“…The ability of multiscale geometric analysis (MGA) theory to process high-dimensional data is better than that of wavelet transform [1]. Since the birth of the JPEG2000 standard, researchers have conducted extensive research on image coding based on the MGA method, among which the most representative research results include image coding based on Ridgelet transform [2][3][4][5], Curvelet transform [6,7], Contourlet transform [8][9][10][11][12][13][14][15][16], Bandelet transform [17,18], and based on directional wavelet transform [19], etc. Besides, due to computational complexity and redundancy problems, these researches focused primarily on Contourlet transform.…”