Research on Image Fractal Compression Coding Algorithm Based on Gene Expression Programming

Li, Wenjing; Pan, Qiuxia; Lǚ, Jianbo; Li, Songzhao

doi:10.1109/dcabes.2018.00032

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…With lossy model the reconstructed image will lose some of its data, at variance lossless technique which keeps the original image as it without data loss. Fractal image compression considered one of common lossy image compression models [11]. In this paper we will produce a near lossless image compression based on fractal theory by computing error values (differences values between the original and reconstructed image).…”

Section: Proposed Solutionsmentioning

confidence: 99%

Semi-lossless Fractal MRI Image Compression Based on Fixed length Technique

Al-Salamee

Al‐Shammary

2021

J. Al-Qadisiyah Comp. Sci. Math.

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Medical image compression plays an essential role to handle large amounts of data for communication and storage purposes. Fractal image compression is a potential lossy compression models with a resulting image that loses some of its information. However, health data communication usually cannot afford any lose for patients visual information. This paper proposes a new high efficiency semi-lossless fractal image compression method (SLFIC) based on fractal theory and fixed length technique. Technically, the resultant lossy fractals compressed image is analyzed and error in comparison with the original image is detected. Then, Fixed-length is developed to compress the detected errors and attached to the compressed image. In practice, a potential performance by the new developed model has been obtained in comparison with two other lossless models: ( Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA) with Linde–Buzo–Gray (LBG) (L2-LBG)) and(Neural Network Radial Basis Function (NNRBF)). Moreover, a high quality that has been obtained by the proposed system based on Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

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Section: Proposed Solutionsmentioning

confidence: 99%

Semi-lossless Fractal MRI Image Compression Based on Fixed length Technique

Al-Salamee

Al‐Shammary

2021

J. Al-Qadisiyah Comp. Sci. Math.

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“…23,24 To this end, people have proposed the parallel algorithm for fractal image compression on a variety of parallel platforms, which greatly improves the compression encoding speed and reduces the encoding time. For example, Wang and Zheng 25 proposed a distributed parallel fractal image compression algorithm in the cluster environment; Li et al 26 proposed the adaptive image fractal compression parallel algorithm based on relative gradient; in multi-core PC platform, Li et al 27 proposed the GEP-based parallel algorithm for fractal image compression. In addition, other scholars have proposed many different parallel algorithms for fractal image encoding.…”

Section: Fractal Image Encodingmentioning

confidence: 99%

“…33 Jedrzejowicz and Wierzbowska 34 proposed the gene expression programming in large data set classification parallel environment. Li et al 27 applied GEP to the application research of fractal image compression, and used GEP's efficient search ability to quickly search the self-similarity of fractal images, which achieved good results. GEP combines the advantages of both genetic algorithms and genetic programming, and is two to four orders of magnitude more efficient than traditional genetic programming methods when solving the complex problems.…”

Section: Gene Expression Programmingmentioning

confidence: 99%

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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