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

Rahman, N. A. Z.; Ismail, Rizalafande Che; Shapri, A. H. M.; Isa, Mohd Nazrin Md

doi:10.1063/1.5142103

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…The domain and range are classified according to the underlying spatiotemporal similarity of the 3D objects. Rahman et al 15 proposed using the adjacent peer to the sum of absolute difference mapping to speed up the full-search fractal image (FIC) while still retaining picture quality. Shivaputra et al 16 used an IWT to compress a DICOM file and then encrypted it with an Advanced Encryption Standard technique before sending it over a TCP/IP network.…”

Section: Related Workmentioning

confidence: 99%

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RETRACTED: Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Abbas

2022

J. Electron. Imag.

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Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space.

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Section: Related Workmentioning

confidence: 99%

“…The domain and range are classified according to the underlying spatiotemporal similarity of the 3D objects. Rahman et al 15 . proposed using the adjacent peer to the sum of absolute difference mapping to speed up the full-search fractal image (FIC) while still retaining picture quality.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Abbas

2022

J. Electron. Imag.

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“…Studies [32]- [34] used the fractal method. Their algorithms are characterised by good results, as the research results show high compression ratios, good quality of the reconstructed image, and a fast and straightforward decoding process.…”

Section: Discussionmentioning

confidence: 99%

“…Then, according to the inherent spatiotemporal resemblance of 3D objects, range and domain blocks are classified. Rahman et al [34] suggested a peer adjacent to the sum of absolute difference (SAD) mapping to improve the speed of the Full-search fractal image (FIC) while still maintaining the quality of the resulting image.…”

Section: Fractal Techniquesmentioning

confidence: 99%

Significant medical image compression techniques: a review

Abduljaleel

Khaleel

2021

TELKOMNIKA

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Telemedicine applications allow the patient and doctor to communicate with each other through network services. Several medical image compression techniques have been suggested by researchers in the past years. This review paper offers a comparison of the algorithms and the performance by analysing three factors that influence the choice of compression algorithm, which are image quality, compression ratio, and compression speed. The results of previous research have shown that there is a need for effective algorithms for medical imaging without data loss, which is why the lossless compression process is used to compress medical records. Lossless compression, however, has minimal compression ratio efficiency. The way to get the optimum compression ratio is by segmentation of the image into region of interest (ROI) and non-ROI zones, where the power and time needed can be minimised due to the smaller scale. Recently, several researchers have been attempting to create hybrid compression algorithms by integrating different compression techniques to increase the efficiency of compression algorithms.

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“…Fractal image compression is the usual example of an asymmetrical strategy. [13]. This algorithm uses image compression less than Huffman Coding to compress the 512×512 image files.…”

Section: Fractal Image Compressionmentioning

confidence: 99%

Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

Naaman

2021

AJRCoS

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Image compression research has increased dramatically as a result of the growing demands for image transmission in computer and mobile environments. It is needed especially for reduced storage and efficient image transmission and used to reduce the bits necessary to represent a picture digitally while preserving its original quality. Fractal encoding is an advanced technique of image compression. It is based on the image's forms as well as the generation of repetitive blocks via mathematical conversions. Because of resources needed to compress large data volumes, enormous programming time is needed, therefore Fractal Image Compression's main disadvantage is a very high encoding time where decoding times are extremely fast. An artificial intelligence technique similar to a neural network is used to reduce the search space and encoding time for images by employing a neural network algorithm known as the “back propagation” neural network algorithm. Initially, the image is divided into fixed-size and domains. For each range block its most matched domain is selected, its range index is produced and best matched domains index is the expert system's input, which reduces matching domain blocks in sets of results. This leads in the training of the neural network. This trained network is now used to compress other images which give encoding a lot less time. During the decoding phase, any random original image, converging after some changes to the Fractal image, reciprocates the transformation parameters. The quality of this FIC is indeed demonstrated by the simulation findings. This paper explores a unique neural network FIC that is capable of increasing neural network speed and image quality simultaneously.

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

Cited by 4 publications

References 23 publications

RETRACTED: Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

RETRACTED: Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Significant medical image compression techniques: a review

Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

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