RIFD Fibonacci Zeckendorf Hybrid Encoding and Decoding Algorithm for Medical Image Compression and Reconstruction

Abstract

Digital medical images are an important source of information that help doctors in diagnosis and treatment. The raw form of a digital image requires a tremendous amount of storage memory and a longer time for transmission from one node to another through a limited bandwidth network. Thus, many algorithms are developed and implemented for image compression that eliminates the redundant information while keeping the essential ones. The decoding algorithms are able to extract this essential information and reconstruct the original images without losing the original image quality. Rounding a pixel’s Intensity Followed by a Division process is called RIFD algorithm. It minimizes the information redundancy of the images and maintains the image visual quality with insignificant distortion. The integration of Fibonacci sequence and Zeckendorf’s theorem produces a simple and fast variable length code for representing integer data. This article proposes a hybrid encoding and decoding algorithm for medical image compression by merging the RIFD and the suffix variable length second order Fibonacci-Zeckendorf codes. Performance metrics such as number of bits per pixel, compression ratio, memory saving percentage, run time, mean square error, peak signal to noise ratio and similarity structure index are used in evaluating the efficiency of the proposed algorithm on nine medical images. The simulation results shows the efficiency of the proposed algorithm especially in compressing images with non uniform distributed histograms.