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

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    Author
    Salem, Nema cc
    Elnaggar, Fathy
    Subject
    medical image compression, Fibonacci codes, Zeckendorf’s theorem, gray levels range reduction, performance metrics, RIDF
    Date
    2020-11-23
    
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    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.
    Department
    Electrical and Computer Engineering
    Publisher
    IEEE
    DOI
    10.1109/MCNA50957.2020.9264295
    ae974a485f413a2113503eed53cd6c53
    10.1109/MCNA50957.2020.9264295
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