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Review of Diverse Techniques Used for Effective Fractal Image Compression
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 407 Review Of Diverse Techniques Used For Effective Fractal Image Compression Shephali D. Wakalkar1, Dr. P. R. Rothe2, Prof. Mr. C. N. Bhoyar3 1 M.Tech Student, Department of Electronics Engineering, P.C.E, Nagpur, Maharashtra, India 2 Assistant Professor, Department of Electronics Engineering, P.C.E, Nagpur, Maharashtra, India 3 Assistant Professor, Department of Electronics Engineering, P.C.E, Nagpur, Maharashtra, India ----------------------------------------------------------------------------------***---------------------------------------------------------------------------- Abstract - The image compression in an image processing plays an important role since the beginning of Internet era and telecommunication. It is necessary for efficient storage and transmission of image. Fractalimagecompression(FIC)is one of the most suitable image compression approachesfor its high compression ratio and quality of retrieved images. Many algorithms are available to compress an image file like Quad tree Partitioning Huffman Coding (QPHC), Discrete Cosine Transform based FIC (DCTFIC), Discrete Wavelet Transform based FIC (DWTFIC), Grover’s quantum search algorithm based FIC (QAFIC)and Tiny Block Size Processing algorithm(TiBS). This paper presents different approach of designing a fractal image compressioninordertoenhancethe compression ratio with low losses in the image. Key Words: Image Processing,DiscreteCosineTransform (DCT), Discrete Wavelet Transform (DWT), FractalImage Compression (FIC), Grover’s Quantum Search Algorithm (QSA), Tiny Block-Size Processing Algorithm (TiBS). 1. INTRODUCTION Images are very useful documentsnowadaysfora numberof applications. They need to be compressed before storing and transmitting, due to limited bandwidth and storage capacity. Image compression plays an important role in multimedia and digital communicationfields. Thepurposeof image compression is to reduce irrelevanceandredundancy of the image data in an efficient form. This not only reduces the storage cost but also increasesthespeedoftransmission. Image compression is divided into two categories which are Lossy as well as Lossless [1]. In lossless compression, the reconstructed image after compression is numerically same as the original image. Thus, it gives good quality of compressed images, but yields only less compression. In lossy compression [2], the reconstructed image contains some degradation comparative to the original due to loss of data with higher compression ratio. For lossless image compression, various approaches available are Variable- Length encoding, AdaptivedictionaryalgorithmssuchasBit- plane coding , LZW coding, lossless predictive coding, etc. For lossy compression, various approaches are lossy predictive coding and transform coding such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) [3]. Fractal image compression (FIC) was firstly proposed by Arnaud E. Jacquin [4]. It is one of the lossy compression technique with high Compression ratio and fast decompression times. The decoding phase is independent of the reconstructed image and the reconstructed image is of good quality [5]. FIC is based on fractal geometry thatmeans split geometric shapes that can be break into parts, each of which is a decreased-size copy of the total, a property called self-similarity [6]. FIC is good for natural images and textures because they exhibits enormous amount of self- similarities. So there is huge work load of searching self- similarities, which lead to FIC rapid development. • In recent years, many FIC algorithms have been proposed, such as discrete cosine transform based FIC (DCT-FIC) [7], Discrete wavelet transform based FIC (DWT-FIC) [8], Baseline FIC etc. But reducing intrinsic computational complexity of FIC is still a problem. Fortunately, L. Grover [9]–[11] invented Grover’s quantum search algorithm (QSA), based on quantum computing. The idea of quantum computing is brought intoFIC,toutilizequantumparticlesas a computational resource in order to reduce search complexity in FIC. C. Zalka [12] proved that Grover’s QSA is precisely best in search problems. This Grover’s quantum search algorithm based FIC (QAFIC) reduces the time complexity of FIC drastically and maintain quality of retrieved images without sacrificing the compression ratio. For the above reasons and motivations, in this research, we try to use this QAFIC algorithm for further improvement. 2. LITERATURE REVIEW The research papers on the design of fractal image compression are published in different journals and presented in many conferences. Utpal Nandi and Jyotsna Kumar Mandal et. al.[13] designed an image compression based on the new fast classification scheme with quadtree partitioning method. In this method, the quadtree partitioning scheme where a range is broken up into four equal sized sub-ranges and the classification scheme 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 sub-ids- 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
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 05 | May -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 408 row code (RC). Similarly, to generate ID for each column, each sub-block are assigned a two bit code out of four possible codes 00, 01, 10 and 11 that are termed as column code (CC). This classification scheme reduces the compression time as compared to the other image compression techniques, also maintain the same compression ratio and peak signal to noise ratio (PSNR). Chong Fu and Zhi-liang Zhu [14] designed a new block classification method based on the edge characteristic of an image block. There are total three steps for the functioning of Discrete Cosine Transform based fractal image compression (DCT-FIC). First one is image partition in which image is partitioned into non overlapping a set of pixels range blocks and overlapping set of pixels domain blocks. Second one is image block classification inwhich the range and domain blocks are divided into three classes based on their DCT lower frequency coefficients and third one is best match exploiting in which onlythedomain blocks match with the range block are calculated. The classification is based on the lower frequency horizontal and vertical DCT coefficients of an image block. This method considerably improves the fractal encoding speed and also satisfied the fidelity of the reconstructed image. Padmavati. S and Dr. Vaibhar Mesharam [15] designed an image compression on hybrid methodology. In this methodology, the lossy and lossless compression methods are combined. Firstly, the given image is compressed using DCT and compression on similar blocks of the image is avoided by fractal quadtree image compression. Finally the image is encoded effectively by using Huffman encoding which improves the quality of the compressed image. The combination of DCT and fractal quadtreedecomposition was successful in terms of reducing the encoding time and maintaining the quality of the image. This technique is also applied to many real time applications such as medical images, satellite images, etc. There is also improvement in compression ratio as compared to the normal fractal compression using quadtree decomposition of image. Mehdi MasoudiChelehgahiandMohsenDerakhshanNia [16] designed an image compression based on high speed intelligent classification algorithm using DCT coefficients. This method is particularly designed to reducethe encoding time. In this method, it reshape the given image into 1D array and calculate the DCT and standard deviation of each row. Ahmad A. Nashat and N. M. Hussain Hassan [17] designed an image compression based upon the Wavelet Transform and the Statistical Threshold. This method is based on the Haar Wavelet transform. The Discrete Wavelet Transform(DWT) of the image is generated by obtaining wavelet decomposition coefficients for the desired levels. The histogram for the selected level is calculated anda threshold for the decomposed image coefficient is selected which is based upon the statistics of the histogram. Then wavelets combines with fractal image in order to get the best results for image compression and decompression. This fractal image compression with wavelet transform can effectively solve the noise problem. Cristian Duran-Faundez, Vincent Lecuire, Francis Lepage were proposed the Tiny block-size coding [18], for energy- efficient image compression andcommunication.TinyBlock- Size algorithm (TiBS) is a lossy compression algorithm. It wiil enhance the compression ratio and also reduces the effects which might occur due to the lossy nature of normal Fractal Image Compression (FIC). Since DCT or DWT is computationally intensive, the Encoder in TiBs does not use DCT or DWT. This algorithm operates on blocks of 2x2 pixels. Each block is encoded independently, based on three stages: uniform scalar quantization, self-adaptive pixel removal, and variable-length coding. 3. CONCLUSIONS The study of papers shows differentapproachesofdesigning the fractal image compression. Fractal Image Compression has been implemented using Quadtree Partitioning with Huffman Coding (QPHC) algorithm,DCT basedFractal Image Compression Algorithm (DCT-FIC), DWT based Fractal Image Compression Algorithm (DWT-FIC), Tiny Block Size Processing based Image CompressionAlgorithm(TiBS) and Grover’s Quantum Search Algorithm based Fractal Image Compression (QAFIC). Especially, QAFIC performs betterfor the images that consist of detailed view and structural similarities. It reduces the intrinsic computational complexity and maintain the quality of retrieved images without sacrificing compression ratio. A new approach can be proposed for image compression where QAFIC is combined with TiBS which shall enhance the compression ratio with low losses in the image. REFERENCES [1] Gonzalez, R. and Eugene, R. “Digital image processing”, 466, 2008. [2] Kaouri, A. H. “Fractal coding of still images”, Queen’s university of Belfast, UK. 2002. [3] Hu, L., Chen, Q. and qing, Z. “An image compression method based on fractal theory”, The 8th international conferenceoncomputersupportedcooperative work in design proceedings, 546 – 550, 2003. [4] A. E. Jacquin, “Image coding based on a fractal theory of iterated contractive image transformations,” IEEE Trans. Image Process, vol. 1, no. 1, pp. 18–30, Jan.1992. [5] N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. Image Process, vol. 9, no. 4, pp. 636–650, Apr. 2000.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 05 | May -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 409 image coding literature”, IEEE Trans. Image Process, vol. 8, no. 12, pp. 1716–1729, Dec. 1999. [7] R. E. Chaudhari and S. B. Dhok, “Wavelet transformed based fast fractal image compression”, in Proc.Int.Conf. Circuits, Systems, Communication and Information Technology Applications (CSCITA), Apr. 2014, pp. 65– 69. [8] C. Fu and Z. Zhu, “A DCT-based fractal image Compression method ”, in Proc.Int.Workshop Chaos Fractals Theories and Applications (IWCFTA), Nov. 2009, pp. 439–443. [9] L. K. Grover, “A fast quantum mechanical algorithm for database search”, in Proc. 28th ACM Symp. Theory of Computing (STOC), May 1996, pp. 212–219. [10] L. K. Grover, “Quantum mechanics helps in searching for a needle in a haystack”, Phys. Rev. Lett., vol. 79, no. 2, pp. 325–328, Jul. 1997. [11] L. K. Grover, “Quantum computers can search rapidly by using almost any transformation”, Phys. Rev. Lett., vol. 80, no. 19, pp. 4329–4332,May 1998. [12] C. Zalka, “Grovers quantum searching algorithm is optimal”, Phys. Rev. A, vol. 60, no. 4, pp. 2746–2751, Oct. 1999. [13] Utpal Nandi and Jyotsna Kumar Mandal et. al.,“Fractal Image Compression with Quadtree Partitioning and a new Fast Classification Strategy”, International Conference IEEE Paper, 2015. [14] Chong Fu and Zhi-liang Zhu “A DCT-based Fractal ImageCompressionMethod”,International Conference IEEE Paper. [15] Padmavati S. and Dr. Vaibhar Mesharam “DCT Combined With Fractal Quadtree Decomposition and Huffman Coding for Image Compression”, International Conference IEEE Paper, 2015. [16] Mehdi Masoudi Chelehgahi et.al. “A High Speed Intelligent Classification Algorithm for Fractal Image Compression using DCT Coefficients”, International Conference IEEE Paper, 2011. [17] Singh H.K., Tomar S.K, Singh P, "Analysis of Multispectral Image Using Discrete Wavelet Transform,"AdvancedComputingandCommunication Technologies (ACCT), 2013 Third International Conference on , vol., no., pp.59,62, 6-7 April 2013. [18] Cristian Duran-Faundez, Vincent Lecuire, Francis Lepage, "Tiny block-size coding for energy-efficient image compression and communication in wireless camera sensor networks", Signal Processing: Image Communication 26 (2011) 466–481. 2011 Elsevier. [6] B. Wohlberg and G. De Jager, “A review of the fractal
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