Fractal Image Compression Using Quadtree Partitioning

Soyjaudah, K. M. S.; Jahmeerbacus, I.

doi:10.7227/ijeee.39.1.7

Cited by 6 publications

(8 citation statements)

References 3 publications

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“…For example, Wang et al 10 proposed a DCT domain fractal image compression encoding method, which maps the images to the DCT domain for encoding; Karthikeyan et al 11 proposed a fractal image compression method based on wavelet analysis. Soyjaudah and Jahmeerbacus 12 proposed a wavelet domain fractal image compression algorithm based on quadtree. Their purpose is to map all the images to the wavelet domain, and then perform the block segmentation on the wavelet domain, thus reducing the search amount and range, which can obtain a better compression ratio.…”

Section: Fractal Image Encodingmentioning

confidence: 99%

“…(2) Another hybrid encoding method is to combine the optimization methods such as genetic algorithm, ant colony algorithm and gene expression programming with fractal images, and use the optimizing precision of optimization algorithm to automatically classify, thus realizing the matching search within the class, improving the encoding speed, and reducing the blocking effect. For example, Soyjaudah et al 12 proposed a fractal image compression method based on genetic algorithm by using the fractal image compression of self-organizing map 13 ; Zhao Deping 14 proposed a fast fractal image compression method based on ant colony algorithm; Menassel et al 15 proposed an improved fractal image compression using wolf pack algorithm, which divides the image space into blocks, and the sliding wolf explores in this space to find other similar smaller blocks. They construct an optimization algorithm for classification search matching, which effectively overcomes the shortcomings of block-based search method to a certain extent and improves the compression ratio and fidelity.…”

Section: Fractal Image Encodingmentioning

confidence: 99%

“…where s(B, x)represents the similarity of the image, shown by formula (12); c(k) represents the compression factor, shown by formula (13); L(n) represents the factors of compressed affine transformation number, shown by formula (14). The parameter B is the image, x is the affine transformation, k is the compression factor, and n is the number of desired compression affine transformations, and the meaning of the parameters and formulas is given in Hinton and Osinde Rosthe.…”

Section: Encoding Of Genes and Individuals (Chromosomes)mentioning

confidence: 99%

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Research on fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming

Pan

Liang

et al. 2019

Journal of Algorithms & Computational Technology

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Regarding the problems of insufficient image segmentation intelligence, low compression rate, slow speed for global searching to find the optimal fractal image compression encoding, and bad decoding effect, this article proposes the fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming. Firstly, according to the accurate and fast image classification of deep convolutional neural network and the fast search and matching encoding advantages of gene expression programming, it realizes theoretically the action mechanism of fractal image compression hybrid encoding by combining the convolutional neural network and the gene expression programming; then, it uses the deep convolutional neural network to train and classify the image, and uses the adaptive quadtree segmentation method to segment the classified image, thus generating the domain block and range block classification set. According to the action mechanism of gene expression programming in fractal image compression encoding, it then quickly obtains the optimal solution of fractal image compression encoding by searching and encoding the sub-blocks of range block classification set and the classification set corresponding to the domain. Finally, in the CPU/GPU environment, it conducts the comparative experiment with basic fractal image compression algorithm and fractal image compression algorithm based on convolutional neural network. The experimental results show that this proposed algorithm outperforms similar algorithms in terms of image segmentation speed and accuracy as well as fractal compression encoding speed and compression ratio. Therefore, this algorithm is a fractal image compression algorithm with intelligent segmentation, fast encoding and high compression ratio.

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Section: Fractal Image Encodingmentioning

confidence: 99%

Section: Fractal Image Encodingmentioning

confidence: 99%

Section: Encoding Of Genes and Individuals (Chromosomes)mentioning

confidence: 99%

See 1 more Smart Citation

Research on fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming

Pan

Liang

et al. 2019

Journal of Algorithms & Computational Technology

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“…The best domain block for the corresponding range block is selected and stored in domain code book. Many researchers are working on this area for suitable selection of domain blocks in order to reduce the encoding time which\ is the major drawback of FIC [15][16][17][18][19][20]..The selected domain blocks are pooled into the domain pool in fractal encoding which is similar to the codebook referred to as virtual codebook or domain codebook. Fractal Image compression provides high Compression Ratio and good Peak Signal to Noise Ratio.…”

Section: Related Workmentioning

confidence: 99%

Images using Embedded Block Coding Optimization Truncation(EBCOT)

Padmashree

Nagapadma²

2015

2015 IEEE International Advance Computing Conference (IACC)

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Images are compressed using lossy and lossless compression schemes to utilize the Bandwidth in an effective way and also to provide enough space for storing voluminous data. Fractal Image Compression (FIC), is one such lossy compression scheme based on contractive mapping theorem. Affine transforms are being employed in Fractal Image compression to map the range blocks and domain blocks incorporating the property of self similarity in the images. As there is a need to enhance the performance measures like compression ratio (CR) and peak signal to noise ratio (PSNR), an attempt is made in this research paper to analyze FIC using Quad-tree Partitioning technique and Embedded Block Coding Optimization Truncation (EBCOT) technique for Medical Images. Here in this paper FIC with EBCOT is being implemented on Medical Images like CT of Bone and MR Images of Brain and thePerformance measures like CR, PSNR at different Threshold Values were measured. Mat lab simulated results for these Performance Measures showed that the PSNR and CR is high when FIC is performed along with EBCOT Encoding.

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“…The set of transformations selected is applied on each Range block to map on to the domain blocks Fractal Image compression provides high Compression Ratio and good Peak Signal to Noise Ratio. There are wide varieties of approaches being implemented by researchers to fasten the encoding process [15][16][17][18][19][20]. These approaches are addressed as Speed-up-Techniques for fractal image compression and broadly classified as Domain Classification Based Methods and Feature Vector Based Methods.…”

Section: Related Workmentioning

confidence: 99%

Performance Measure Analysis between Anisotropic Diffusion Filter and Bilateral Filter for Post Processing of Fractal Compressed Medical Images

Padmashree¹,

Nagapadma²

2015

IJCA

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Filtering is Prime important processes in Medical Image processing applications. Any post processing process aims in the removal of unwanted noise which usually corrupts the image quality and perception. This research paper focuses on searching effective De noising filters for post processing of Fractal compressed Images on Medical Images like CT of Bone , MR Images of Brain ,Mammograms, Ultrasound Images of uterus. In this work Fractal Image Compression (FIC) a lossy compression scheme based on contractive mapping theorem is employed to map the Range blocks and Domain blocks by using the property of self similarity in the images. We have used two types of filters namely anisotropic diffusion filter and bilateral filter for the removal of noise in Medical images. The Peak signal to noise ratio (PSNR) was measured after applying the two different filters and a comparative analysis of PSNR values before and after filtering was recorded. The simulated results obtained showed an increase in PSNR value for bilateral filter than with anisotropic filter and also the quality of the image was improved.

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Fractal Image Compression Using Quadtree Partitioning

Cited by 6 publications

References 3 publications

Research on fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming

Research on fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming

Images using Embedded Block Coding Optimization Truncation(EBCOT)

Performance Measure Analysis between Anisotropic Diffusion Filter and Bilateral Filter for Post Processing of Fractal Compressed Medical Images

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