Parameter analysis of fractal image compression and its applications in image sharpening and smoothing

Wang, Jianji; Zheng, Nanning; Liu, Yuehu; Zhou, Gang

doi:10.1016/j.image.2012.12.006

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…Recently, researchers have proposed many image enhancement methods [ [28]. The histogram equalization method can improve the image quality, but it often brings some discontinuous patches into the image.…”

Section: The Pre-processing Of Gamma Correctionmentioning

confidence: 99%

Gamma correction FCM algorithm with conditional spatial information for image segmentation

2018

KSII TIIS

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Fuzzy C-means (FCM) algorithm is a most usually technique for medical image segmentation. But conventional FCM fails to perform well enough on magnetic resonance imaging (MRI) data with the noise and intensity inhomogeneity (IIH). In the paper, we propose a Gamma correction conditional FCM algorithm with spatial information (GcsFCM) to solve this problem. Firstly, the pre-processing, Gamma correction, is introduced to enhance the details of images. Secondly, the spatial information is introduced to reduce the effect of noise. Then we introduce the effective neighborhood mechanism into the local space information to improve the robustness for the noise and inhomogeneity. And the mechanism describes the degree of participation in generating local membership values and building clusters. Finally, the adjustment mechanism and the spatial information are combined into the weighted membership function. Experimental results on four image volumes with noise and IIH indicate that the proposed GcsFCM algorithm is more effective and robust to noise and IIH than the FCM, sFCM and csFCM algorithms.

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Section: The Pre-processing Of Gamma Correctionmentioning

confidence: 99%

Gamma correction FCM algorithm with conditional spatial information for image segmentation

2018

KSII TIIS

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“…The proposed method is 100 times faster than the full search method. Fractal image compression [18] is used in image sharpening and image smoothing. The affine parameters of FIC can vary with different image quality measurements, and they also directly affect the visual quality of a decoded image.…”

Section: Literature Reviewmentioning

confidence: 99%

Fractal based Image Compression Techniques

Kadam¹,

Rathod²

2017

IJCA

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Fractal image compression offers high compression ratios and quality image reconstruction. It uses various techniques as the fractal with DCT, wavelet, neural network, genetic algorithms, quantum acceleration etc. Additionally, because fractals are infinitely magnifiable, fractal compression is resolution independent and so a single compressed image can be used efficiently for display in any image resolution including resolution higher than the resolution of the original image. Breaking an image into pieces and identifying selfsimilar ones is the main principle of the approach. In this paper, the different issues in fractal image compression as partitioning, larger encoding time, compression ratio, quality of the reconstructed image, decoding time, SSIM(Structured Similarity Index) are discussed and highlighted. The various areas for improvement as larger encoding time and PSNR are also suggested. The various parameters for evaluating the performance of these techniques as PSNR, compression ratio, encoding time, and decoding time are also suggested. Comparison of Fractal techniques for color image, texture and satellite image is done using different parameters as compression time, compression ratio and PSNR. The hybrid method which combines Fractal quad tree with wavelet and Huffman coding is implemented and compared different parameters as compression ratio and the compression time of the proposed method with the existing methods.

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“…Since the original fractal image compression scheme was proposed by Barnsley and Jacquin [ 1 , 2 ], FIC technology had demonstrated rapid development. Aside from the application as an image compression technology, FIC has been widely used in some other fields, such as image denoising [ 3 , 4 ], image encryption [ 5 , 6 ], image sharpening [ 7 ], and facial image recognition [ 8 ]. To date, FIC is still one of the research hotspots in image processing.…”

Section: Introductionmentioning

confidence: 99%

Fast sparse fractal image compression

Wang

Chen

et al. 2017

PLoS ONE

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As a structure-based image compression technology, fractal image compression (FIC) has been applied not only in image coding but also in many important image processing algorithms. However, two main bottlenecks restrained the develop and application of FIC for a long time. First, the encoding phase of FIC is time-consuming. Second, the quality of the reconstructed images for some images which have low structure-similarity is usually unacceptable. Based on the absolute value of Pearson’s correlation coefficient (APCC), we had proposed an accelerating method to significantly speed up the encoding of FIC. In this paper, we make use of the sparse searching strategy to greatly improve the quality of the reconstructed images in FIC. We call it the sparse fractal image compression (SFIC). Furthermore, we combine both the APCC-based accelerating method and the sparse searching strategy to propose the fast sparse fractal image compression (FSFIC), which can effectively improve the two main bottlenecks of FIC. The experimental results show that the proposed algorithm greatly improves both the efficiency and effectiveness of FIC.

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Parameter analysis of fractal image compression and its applications in image sharpening and smoothing

Cited by 13 publications

References 32 publications

Gamma correction FCM algorithm with conditional spatial information for image segmentation

Gamma correction FCM algorithm with conditional spatial information for image segmentation

Fractal based Image Compression Techniques

Fast sparse fractal image compression

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