International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
Review On Fractal Image Compression Techniques Ku. Pallavi C. Raut1, Associate Prof. P. R. Indurkar2, Associate Prof. A. W. Hingnikar3 M.Tech Student, Department of Electronics and Telecommunication, B.D.C.O.E, Wardha, Maharashtra, India Assistant Professor, Department of Electronics and Telecommunication, B.D.C.O.E, Wardha, Maharashtra, India 3 Assistant Professor, Department of Electronics and Telecommunication, B.D.C.O.E, Wardha, Maharashtra, India 1
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Abstract - The image processing techniques plays an
important role with the advancement of technology. It finds application in areas where efficient storage and transmission of image is necessary. Fractal coding is a potential image compression scheme which has the advantages of relatively high compression ratios and good reconstruction fidelity. Many methods are available to compress an image file like discrete cosine transform (DCT), discrete wavelet transform (DWT) and fractals. This paper presents different approaches of designing a fractal image compression based on different methods. Key Words: Image processing, discrete cosine transform (DCT), discrete wavelet transform (DWT), Fractal image compression.
1.INTRODUCTION Images are very important documents nowadays to work with them in some applications they need to be compressed more or less depending on the purpose of the application. Due to limited bandwidth and storage capacity, images must be compressed before storing and transmitting. Image compression is an essential technology in multimedia and digital communication fields. Image compression is an application of data compression that encodes the original image with few bits. The objective of image compression is to reduce the redundancy of the image and to store or transmit it in an efficient form. There are two types of image compression Lossy as well as Lossless. In lossless compression, the reconstructed image is numerically similar than that of the original image where as in lossy compression the reconstructed image contain some degradation. But this provides greater compression ratios than lossless technique. Fractal Image Compression has generated much interest due to its promise of high compression ratios and also the advantages of very fast decompression. It is one of the lossy compression technique used in digital images. As the name Š 2017, IRJET
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indicates it is mainly based on the fractals. This approach is good for natural images and textures. In fractal coding, the image is divided into two sub-blocks with different size. One is called Range block (R) and the other is Domain block (D). R blocks do not overlap mutually covering the entire image while D blocks can overlap mutually and the length is twice of R blocks. This fractal image compression with wavelet transform can effectively solve the noise problem.
2. LITERATURE REVIEW The research papers on the design of fractal image compression are published in various journals and presented in many conferences. Here the paper selected describes the design of fractal image compression based on DCT or DWT. Some of the papers are successful to give high compression ratio and some of these gave less encoding time. Utpal Nandi and Jyotsna Kumar Mandal et. al.[1] designed an image compression based on the new fast classification strategy with quadtree partitioning technique. This classification strategy reduces the compression time significantly of the fractal image compression technique maintaining the same compression ratio and peak signal to noise ratio (PSNR). One is quadtree partitioning scheme where a range is broken up into four equal sized sub-ranges and another one is the classification strategy divides square block of image (range/domain) into 16 sub-block. For each block, a 64 bit ID is generated. The ID has row part and column part each of 32 bits. The row part has four 8 bit subids- ID1, ID2, ID3 and ID4. To generate ID for each row, each sub-block are assigned a two bit code out of four possible codes 00, 01,10 and 11 that are termed as row code (RC). Similarly, to generate ID for each column, each sub-block B are assigned a two bit code out of four possible codes 00, 01, 10 and 11 that are termed as column code (CC). It reduces the compression time as compared to the other image compression techniques. Chong Fu and Zhi-liang Zhu [2] designed a new block classification method based on the edge characteristic of an image block. There are total three steps for the ISO 9001:2008 Certified Journal
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