Norazeani Abdul Rahman
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Norazeani Abdul Rahman
Official Name
Norazeani, Abdul Rahman
Alternative Name
Rahman, Norazeani Abdul
Rahman, N. A.
Rahman, Norazeani A.
Rahman, N. A.Z.
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Scopus Author ID
16833887100

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  • Publication
    Enhancing fractal image compression speed using peer adjacent mapping with sum of absolute difference for computed radiography images
    ( 2020)
    Norazeani Abdul Rahman
    ;
    Rizalafande Che Ismail
    ;
    Ahmad Husni Mohd Shapri
    ;
    Mohd Nazrin Md Isa
    The encoding phase in full-search fractal image compression (FIC) is time-intensive as a sequential search must be performed through a massive domain pool to find the best-matched domain for each block of ranges. In this paper, a peer adjacent with the sum of absolute difference (SAD) mapping has been suggested to enhance the FIC speed while retaining the reconstructed image quality. The SAD similarity measure applied in searching the most matching domain between domain pool for a range before transformation in order to shorten the mapping process. Therefore, instead of performing a complete search in the next level, one requires to only search a close neighbourhood of the region computed from the previous search. The efficiency of the proposed method is evaluated using standard test image, SMPTE test pattern and standard computed radiography digital images from JSRT database, from which the peak signal-to-noise ratio (PSNR), compression time and compression ratio are calculated. The experimental results validate the effectiveness of the proposed method. © 2020 Author(s).
  • Publication
    Peer adjacent mapping with optimum parameters for fractal image compression on medical images
    ( 2020)
    Norazeani Abdul Rahman
    ;
    Rizalafande Che Ismail
    ;
    Ahmad Husni Mohd Shapri
    ;
    Mohd Nazrin Md Isa
    In order to reduce fractal image compression (FIC) computational complexity, this paper presents a novel approach based on peer adjacent and mean difference method in mapping the domain and range blocks. It allows the substitution of the costly process of the domain and range mapping with straight-forward peer adjacent schemes. The optimum parameters are suggested in terms of quadtree threshold, range sizes and number of iterations to fine-tune the output. Simulations on real computed radiography images show that the proposed method with optimum parameters yields a considerable peak signal-to-noise ratio (PSNR) value above 48 dB, reducing the runtime by as much as 19.32 s with the highest compression ratio is 20.03. Comparison with the standard FIC method confirms that our method not only accelerated the domain-range mapping procedure but also provided a high compression ratio with remarkable visual quality.
  • Publication
    An Improved Irreversible Fractal Scheme for Medical Image Compression
    ( 2020-12-18)
    Norazeani Abdul Rahman
    ;
    Rizalafande Che Ismail
    ;
    Ahmad Husni Mohd Shapri
    In this paper, an improved fractal image compression (FIC) based on peer adjacent scheme and domain classification was proposed. The proposed method has low computation cost since it contains no search operations, thus becoming fast irreversible fractal scheme. Comprehensive experiments on a standard test image and several types of digital radiology images revealed that the proposed method is competitive when compared to established quadtree-based FIC techniques. The novelty of the proposed method lies in the use of this improved domain classification and mapping strategy for accurate and more precise FIC encoding. The empirical result of standard test image suggests that the proposed method is more competitive compared to the established schemes and achieves better performance in terms the peak signal-to-noise ratio (PSNR) and compression time averaging at 27.27 dB and 6.88 s, respectively. Also, the proposed method obtains an efficient compression ratio with 16.13 compared to others. Additionally, experiments involving various medical image modalities confirmed the superiority of the proposed method for practical applications of medical image compression.
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