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Fractal Image Coding with Digital Watermarks
M. Candik,D. Levicky,Z. Klenovicova
Radioengineering , 2000,
Abstract: In this paper are presented some results of implementation of digitalwatermarking methods into image coding based on fractal principles. Thepaper focuses on two possible approaches of embedding digitalwatermarks into fractal code of images - embedding digital watermarksinto parameters for position of similar blocks and coefficients ofblock similarity. Both algorithms were analyzed and verified on grayscale static images.
Trigonometric Approximation in Fractal Image Coding
M. Candik
Radioengineering , 2001,
Abstract: In this paper is presented a new approach in fractal image codingbased on trigonometric approximation. The least square approximationmethod is used for approximation of blocks in standard fractal imagecompression algorithm. In the paper is shown that it is possible to usealso trigonometric approximation for describing of blocks in fractalimage coding. This approximation was implemented and analyzed frompoint of view of quality of reconstructed images. The experimentalresults of this method were tested on static grayscale images.
Some Modifications of Fractal Image Coding
D. Levicky,M. Candik,R. Pundzak
Radioengineering , 1999,
Abstract: In this paper some modifications of fractal image coding are presented. Proposed methods are based on correlation coefficients computing as an alternative approach to searching of similarity between blocks. The convergence speed of decoding process is faster then convergence speed of standard method. The convergence process with modified start conditions of decoding process are analysed and verified on gray scale static images too.
Fractal Image Coding
分形图象编码

He Aijun,Ma Zhengming,
何爱军
,马争鸣

中国图象图形学报 , 1999,
Abstract: This is an overview on fractal image coding. On the base of collecting and reading related documents, we describe the current research status of fractal image coding and make a remark on its prospect.
Several Remarks on Fractal Image Block Coding
L. Dedera,J. Chmurny
Radioengineering , 1997,
Abstract: In this paper the dependence of PSNR on the size of both a codebook pool and range blocks is compared. The statistical properties of the values of transformation coefficients and distances between coded range blocks and optimal domain blocks in the fractal image block coding scheme are discussed.
Fractal coding based on image local fractal dimension
Conci, Aura;Aquino, Felipe R.;
Computational & Applied Mathematics , 2005,
Abstract: fractal codification of images is based on self-similar and self-affine sets. the codification process consists of construction of an operator which will represent the image to be encoded. if a complicated picture can be represented by an operator then it will be transmitted or stored very efficiently. clearly, this has many applications on data compression. the great disadvantage of the automatic form of fractal compression is its encoding time. most of the time spent in construction of such operator is due on finding the best match between parts of the image to be encoded. however, since the conception of automatic fractal image compression, researches on improvement of the compression time are widespread. this work aims to provide a new idea for decrease the encoding time: a classification of image parts based on their local fractal dimension. the idea is implemented on two steps. first, a preprocessing analysis of the image identify the complexity of each image block computing its dimension. then, only parts within the same range of complexity are used for testing the better self-affine pairs, reducing the compression time. the performance of this proposition, is compared with others fractal image compression methods. the points considered are image fidelity, encoding time and amount of compression on the image file.
Fractal coding based on image local fractal dimension  [cached]
Aura Conci,Felipe R. Aquino
Computational and Applied Mathematics , 2005,
Abstract: Fractal codification of images is based on self-similar and self-affine sets. The codification process consists of construction of an operator which will represent the image to be encoded. If a complicated picture can be represented by an operator then it will be transmitted or stored very efficiently. Clearly, this has many applications on data compression. The great disadvantage of the automatic form of fractal compression is its encoding time. Most of the time spent in construction of such operator is due on finding the best match between parts of the image to be encoded. However, since the conception of automatic fractal image compression, researches on improvement of the compression time are widespread. This work aims to provide a new idea for decrease the encoding time: a classification of image parts based on their local fractal dimension. The idea is implemented on two steps. First, a preprocessing analysis of the image identify the complexity of each image block computing its dimension. Then, only parts within the same range of complexity are used for testing the better self-affine pairs, reducing the compression time. The performance of this proposition, is compared with others fractal image compression methods. The points considered are image fidelity, encoding time and amount of compression on the image file.
Comparative Study: Block Truncating Coding, Wavelet and Fractal Image Compression  [PDF]
Dinesh Gupta,Pardeep Singh,,Nivedita,Sugandha Sharma
International Journal of Computer Technology and Applications , 2012,
Abstract: We undertake a study of the performance difference of different transform coding techniques i.e. Block truncating coding, wavelet and fractal image compression. This paper focuses important features of transform coding in compression of still images, including the extent to which the quality of image is degraded by the process of compression and decompression. The above techniques have been successfully used in many applications. The techniques are compared by using the performance parameters PSNR, CR and reduced size. Images obtained with those techniques yield very good results
Color Image Compression with Modified Fractal Coding on Spiral Architecture  [cached]
Nileshsingh V. Thakur,O. G. Kakde
Journal of Multimedia , 2007, DOI: 10.4304/jmm.2.4.55-66
Abstract: The proposed approach (CICMFCSA), firstly, compose the one-plane image using the pixel’s trichromatic coefficients. One-plane image in traditional square structure is represented in Spiral Architecture for compression. On this Spiral Architecture image, proposed modified Fractal grey level image coding algorithm (MFCSA) is applied to get encoded image. In this modified Fractal coding, the numbers of domain blocks are optimized from 343 to 10 using local search. Extensive experiments are carried out on UCID - An Uncompressed Color Image Database. The proposed approach minimizes the encoding process time because of optimized domain blocks and one dimensional structure of Spiral Architecture and falls in the lossy compression category. The results of SFC and our approach are compared with respect to the time.
Fractal Image Compression Using Quadtree Decomposition and Huffman Coding  [PDF]
Veenadevi.S.V,A.G.Ananth
Signal & Image Processing , 2012,
Abstract: Fractal image compression can be obtained by dividing the original grey level image into unoverlappedblocks depending on a threshold value and the well known techniques of Quadtree decomposition. By usingthreshold value of 0.2 and Huffman coding for encoding and decoding of the image these techniques havebeen applied for the compression of satellite imageries. The compression ratio (CR) and Peak Signal toNoise Ratio (PSNR) values are determined for three types of images namely standard Lena image, SatelliteRural image and Satellite Urban image. The Matlab simulation results show that for the Quad treedecomposition approach shows very significant improvement in the compression ratios and PSNR valuesderived from the fractal compression with range block and iterations technique. The results indicatethat for a Lena image C R is 2.02 and PSNR values is 29.92, Satellite Rural image 3.08 and 29.34,Satellite urban image 5.99 and 28.12 respectively The results are presented and discussed in this paper.
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