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Greylevel Difference Classification Algorithm inFractal Image Compression
Greylevel Difference Classification Algorithm in Fractal Image Compression

Chen Yisong,Lu Jian,Sun Zhengxing,Zhang Fuyan,
陈毅松
,卢坚,孙正兴,张福炎

计算机科学技术学报 , 2002,
Abstract: This paper proposes the notion of a greylevel difference classification algorithm in fractal image compression. Then an example of the greylevel difference classification algorithm is given as an improvement of the quadrant greylevel and variance classification in the quadtree-based encoding algorithm. The algorithm incorporates the frequency feature in spatial analysis using the notion of average quadrant greylevel difference, leading to an enhancement in terms of encoding time, PSNR value and compression ratio. This work is supported by the National Natural Science Foundation of China (No.69903006).
Approximation Error Based Suitable Domain Search for Fractal Image Compression
Vijayshri Chaurasia,Ajay Somkuwar
International Journal of Engineering Science and Technology , 2010,
Abstract: Fractal Image compression is a very advantageous technique in the field of image compression. The coding phase of this technique is very time consuming because of computational expenses of suitable domain search. In this paper we have proposed an approximation error based speed-up technique with the use of feature extraction. Proposed scheme reduces the number of range-domain comparisons with significant amount and gives improved time performance.
Modified Fast Fractal Image Compression Algorithm in spatial domain  [PDF]
M. Salarian,H. Miar Naimi
Computer Science , 2015,
Abstract: In this paper a new fractal image compression algorithm is proposed in which the time of encoding process is considerably reduced. The algorithm exploits a domain pool reduction approach, along with using innovative predefined values for contrast scaling factor, S, instead of searching it across [0,1]. Only the domain blocks with entropy greater than a threshold are considered as domain pool. As a novel point, it is assumed that in each step of the encoding process, the domain block with small enough distance shall be found only for the range blocks with low activity (equivalently low entropy). This novel point is used to find reasonable estimations of S, and use them in the encoding process as predefined values, mentioned above, the remaining range blocks are split into four new smaller range blocks and the algorithm must be iterated for them, considered as the other step of encoding process. The algorithm has been examined for some of the well-known images and the results have been compared with the state-of-the-art algorithms. The experiments show that our proposed algorithm has considerably lower encoding time than the other where the encoded images are approximately the same in quality.
An improved fast fractal image compression using spatial texture correlation

Wang Xing-Yuan,Wang Yuan-Xing,Yun Jiao-Jiao,

中国物理 B , 2011,
Abstract: This paper utilizes a spatial texture correlation and the intelligent classification algorithm (ICA) search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.
Arquitectura Simple y Modular para Compresión Fractal de Imágenes utilizando árbol Cuádruple Multi-Resolución
Martínez,Alejandro; Díaz,Alejandro; Linares,Mónico; Vega,Javier;
Información tecnológica , 2006, DOI: 10.4067/S0718-07642006000100010
Abstract: this work presents a simple and rapid architecture for fractal image compression based on a multi-resolution fractal image compression method, using pyramidal quad-tree partition and a block classification scheme based on image size and contrast. the use of expanded range blocks, non-contracted domain blocks, and chessboard-type block decimation compensated for loss of image quality and allowed obtaining the fractal parameters at low image resolution without recalculating them. the proposed architecture takes advantage of the simplicity of the method to obtain a modular structure. because all the range blocks are equal-sized through multi-resolution image pyramid structure, a single type of fractal-coding module can carry out the all the quad-tree fractal encoding.
Genetic algorithm applied to fractal image compression  [PDF]
Y. Chakrapani,K. Soundera Rajan
Journal of Engineering and Applied Sciences , 2009,
Abstract: In this paper the technique of Genetic Algorithm (GA) is applied for Fractal Image Compression (FIC). With the help of this evolutionary algorithm effort is made to reduce the search complexity of matching between range block and domain block. One of the image compression techniques in the spatial domain is Fractal Image Compression but the main drawback of FIC is that it involves more computational time due to global search. In order to improve the computational time and also the acceptable quality of the decoded image, Genetic algorithm is proposed. Experimental results show that the Genetic Algorithm is a better method than the traditional exhaustive search method.
Support Vector Machine for Fast Fractal Image Compression Base on Structure Similarity  [cached]
Chih-Ming Kung,Shu-Tsung Chao
Journal of Software , 2010, DOI: 10.4304/jsw.5.7.777-784
Abstract: Fractal image compression is promising both theoretically and practically. The encoding speed of the traditional full search method is a key factor rendering the fractal image compression unsuitable for real-time application. The primary objective of this paper is to investigate the comprehensive coverage of the principles and techniques of fractal image compression, and describes the implementation of a pre-processing strategy that can reduce the full searching domain blocks by training the Support Vector Machine which could recognized the self-similar pattern feature to enhance the domain block searching efficiency. In this paper, the novel image quality index (Structure Similarity, SSIM) and block property classifier based on SVM employed for the fractal image compression is investigated. Experimental results show that the scheme speeds up the encoder 15 times faster and the visual effect is better in comparison to the full search method.
Arquitectura Simple y Modular para Compresión Fractal de Imágenes utilizando árbol Cuádruple Multi-Resolución Simple and Modular Architecture for Fractal Image Compression using Quad-Tree Multi-Resolution  [cached]
Alejandro Martínez,Alejandro Díaz,Mónico Linares,Javier Vega
Información Tecnológica , 2006,
Abstract: En este trabajo se presenta una arquitectura simple y rápida para compresión fractal de imágenes basada en un método para compresión fractal multi-resolución de imagen, utilizando particionamiento en árbol cuádruple piramidal y un esquema de clasificación de bloques de acuerdo a su tama o y a su contraste. El uso de bloques rangos expandidos, bloques dominios no contraídos, y decimación de bloques tipo tablero de ajedrez compensa pérdidas en la calidad de imagen y permite obtener los parámetros fractales en imágenes de baja resolución sin recalcularlos. La arquitectura propuesta aprovecha la sencillez del método para obtener una estructura modular. Debido a que todos los bloques rango son del mismo tama o a través de la estructura piramidal multi-resolución de la imagen, únicamente un tipo de módulo realiza completamente la codificación fractal en árbol cuádruple. This work presents a simple and rapid architecture for fractal image compression based on a multi-resolution fractal image compression method, using pyramidal quad-tree partition and a block classification scheme based on image size and contrast. The use of expanded range blocks, non-contracted domain blocks, and chessboard-type block decimation compensated for loss of image quality and allowed obtaining the fractal parameters at low image resolution without recalculating them. The proposed architecture takes advantage of the simplicity of the method to obtain a modular structure. Because all the range blocks are equal-sized through multi-resolution image pyramid structure, a single type of fractal-coding module can carry out the all the quad-tree fractal encoding.
Architecture Proposed for New Method of Partitioning High Speed Fractal Image Compression
B. Sankaragomathi,L. Ganesan,S. Arumugam
Journal of Engineering and Applied Sciences , 2012,
Abstract: The main problem of Fractal Image Coding (FIC) is the tremendous encoding time needed due to the large amount of comparisons between range and domain blocks. To overcome this problem, a few dedicated architectures proposed have utilized global data communication for providing domain blocks to all the processors. As the number of processors increases, expanding non-local communication paths is difficult without slowing down the system clock. In this study, we propose an efficient VLSI architecture for Fixed-size Partitioning Fractal Image Compression (FPFIC), which uses only local communication. The main feature of this architecture is that it is capable of performing the fractal image encoding without the external memory for the fixed domain blocks.
A Comparative Approach to Fractal image Compression Using Genetic Algorithm and Simulated Annealing Technique
Y. Chakrapani,K. Soundera Rajan
Asian Journal of Information Technology , 2012,
Abstract: In this study, the technique of Genetic Algorithm (GA) and Simulated Annealing (SA) is applied for Fractal Image Compression (FIC). With the help of these evolutionary algorithms effort is made to reduce the search complexity of matching between range block and domain block. One of the image compression techniques in the spatial domain is fractal image compression but the main drawback of FIC is that it involves more computational time due to global search. In order to improve the computational time and also the quality of the decoded image, genetic and simulated annealing algorithms are proposed. Experimental results show that the genetic algorithm is a better method than Simulated Annealing Technique for fractal image compression.
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