Performance of Fractal Image Compression for Medical Images: A Comprehensive Literature Review

KumarBiswas, Amit; Karmakar, S.; Sharma, Sanjay Kumar; Kowar, M. K.

doi:10.5120/ijais15-451302

Cited by 7 publications

(3 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compare to time, image quality sacrifices a lot, and a single quality measure is not sufficient to confirm image quality. As an approach to medical image compression using fractal theory, Biswas et al (2015) suggests combining the fractal dimension and fuzzy logic still ET(s) is very high. Panigrahy et al (2016) proposed an idea where the coder performed by rotating and reflecting each range block instead of the contracted and intensity-adjusted domain block.…”

Section: Literature Review On Most Recent Papersmentioning

confidence: 99%

Competitive Improvement of the Time Complexity to Encode Fractal Image: By Applying Symmetric Central Pixel of the Block

et al. 2021

View full text Add to dashboard Cite

Section: Literature Review On Most Recent Papersmentioning

confidence: 99%

Competitive Improvement of the Time Complexity to Encode Fractal Image: By Applying Symmetric Central Pixel of the Block

et al. 2021

View full text Add to dashboard Cite

“…However, the PFI based image interpolation usually achieves good subjective performance, which is one of the reasons why fractal image interpolation algorithms have been adopted into many commercial image enlargement software, and is still actively investigated in literature [2][3][4][5][6]. It has been reported in [7,8] that, for some image interpolation applications, such as medical image interpolation, the preservation of image shape (structure) is far more important than the objective quality of the interpolated image. As a result, Ref.…”

Section: Introductionmentioning

confidence: 99%

Fractal Image Interpolation: A Tutorial and New Result

Kok¹,

Tam²

2019

Fractal Fract

View full text Add to dashboard Cite

This paper reviews the implementation of fractal based image interpolation, the associated visual artifacts of the interpolated images, and various techniques, including novel contributions, that alleviate these awkward visual artifacts to achieve visually pleasant interpolated image. The fractal interpolation methods considered in this paper are based on the plain Iterative Function System (IFS) in spatial domain without additional transformation, where we believe that the benefits of additional transformation can be added onto the presented study without complication. Simulation results are presented to demonstrate the discussed techniques, together with the pros and cons of each techniques. Finally, a novel spatial domain interleave layer has been proposed to add to the IFS image system for improving the performance of the system from image zooming to interpolation with the preservation of the pixel intensity from the original low resolution image.

show abstract

“…Amit Kumar Biswas et al proposed Fractal image compression algorithm to encode the image. This algorithm enhance the reconstructed quality of image with high compression ratio [1]. Sharmila et al proposed (DCT) Discrete Cosine Transform to compress the image.…”

Section: Introductionmentioning

confidence: 99%

A New Modified Fast Fractal Image Compression Algorithm in DCT Domain

Jiji¹,

Herlin²,

Singerji³

et al. 2017

IJERT

View full text Add to dashboard Cite

Medical Image Processing is the technique of analysing medical image such as CT, MRI for diagnostic and treatment purpose. The role of image compression is vital in domain of tele medicine for storage and transmission of medical data. In this project transform domain based compression algorithm is employed for the efficient compression and transfer of medical data. The Discrete Cosine Transform (DCT) based fractal image compression algorithm is employed for real time CT medical image. The result is validated by performance metrics Peak Signal to Noise Ratio (PSNR) and compression ratio. The algorithm was developed in MATLAB 2010a.

show abstract

Performance of Fractal Image Compression for Medical Images: A Comprehensive Literature Review

Cited by 7 publications

References 96 publications

Competitive Improvement of the Time Complexity to Encode Fractal Image: By Applying Symmetric Central Pixel of the Block

Competitive Improvement of the Time Complexity to Encode Fractal Image: By Applying Symmetric Central Pixel of the Block

Fractal Image Interpolation: A Tutorial and New Result

A New Modified Fast Fractal Image Compression Algorithm in DCT Domain

Contact Info

Product

Resources

About