Ahmad Husni Mohd Shapri
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Preferred name
Ahmad Husni Mohd Shapri
Official Name
Ahmad Husni, Mohd Shapri
Alternative Name
Mohd Shapri, Ahmad Husni
Mohd. Shapri, Ahmad Husni
Shapri, Ahmad Husni Mohd
Shapri, Ahmad H.M.
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Scopus Author ID
55533640800
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DTB-1911-2022

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Enhancing fractal image compression speed using peer adjacent mapping with sum of absolute difference for computed radiography images

2020 , N. A. Z. 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.

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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.

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