, Mr. Amarsinh B. Farakte, Mr. S.B. Patil, Miss Mohini B. Rasale

English

International Journal on Recent and Innovation Trends in Computing and Communication

International Journal on Recent and Innovation Trends in Computing and Communication                                       ISSN: 2321-8169 Volume: 2 Issue: 7                                                                                                                                                                       2002 – 2006 _______________________________________________________________________________________________ 2002 IJRITCC | July 2014, Available @ http://www.ijritcc.org                                                                 _______________________________________________________________________________________ Performance Measurement of Huffman Coding based Improved SPIHT Image compression AlgorithmMr. Amarsinh B. Farakte Electronics & Telecommunication Engg. SGMCOE, Mahagaon Gadhinglaj (MH), India amar_farakte@rediffmail.com Mr. S.B. Patil Electronics & Telecommunication Engg D.Y.P.C.E.T. Kasaba Bawada Kolhapur (MH), India    s_b_patil200@rediffmail.com    Miss Mohini B. Rasale Electronics & Telecommunication Engg D.Y.P.C.E.T. Kasaba Bawada Kolhapur (MH), India mohini_rasale@yahoo.co.inAbstract-Recent development in image processing requires a compression algorithm with high speed and better performance in terms of quality matrices. In this paper, an improved SPIHT algorithm based on Huffman coding for image compression are presented. The results are successfully tested on different gray scale images by changing the compression rate.  The best results are obtained by keeping decomposition level at five. The traditional SPIHT algorithm degrades the performance in terms of PSNR and Compression Ratio. The Huffman coding based improved SPIHT algorithm gives better performance as compared with traditional SPIHT algorithm. The input gray scale image decomposed by wavelet filter type “bior4.4” and relative wavelet coefficients are indexed and scanned by DWT, then applied to encode the coefficient by SPIHT encoder. The generated image code vector of type UINT8 again encoded by Huffman Encoder which gives the compressed image in the form of encoded data. At the other side, reverse process carried out to get the reconstructed image.   Keywords- PSNR, MSE, Compression Ratio, DWT, IDWT. __________________________________________________*****_________________________________________________  I. INTRODUCTION Image compression addresses the reduction in the amount of data required to represent the digital image. Image Compression is achieved by the removal of one or more of three basic data redundancies as listed;  (1) Coding redundancy, which is present when less than best possible code words are used;  (2) Inter pixel redundancy, which results from correlation between the pixels of an image;  (3)  Psycho visual redundancy, which is due to data that is unnoticed by the human visual system. The multimedia files (graphics, audio and video) require substantial storage capacity and broadcast bandwidth. As the rapid progress in digital communication system performance, demand very high space for data storage capacity and data transmission bandwidth continuous to surpass the capabilities of available compression schemes. The recent growth of data demanding multimedia-based web page applications have not only sustained the need for more competent ways to encode signals and images but have made compression of such signals essential to store in communication technology.  Huffman coding is a form of statistical coding technique. The codes contain smallest possible number of code symbols per source symbol (e.g. grey level value) subject to the constraint that the source symbols are coded one at a time. Huffman coding more frequently used symbols have shorter code words and works well for text and fax transmissions. Huffman coding gives better performance in terms of Compression Ratio and PSNR when combined with SPIHT.  In this paper the combined compression scheme are discussed. Section 2 summarizes the present compression schemes. We propose Huffman based Improved SPIHT algorithm structure in Section 3 and Quality Evaluation in Sect. 4, Section 5 summarizes Results and Discussion. Conclusions are presented in Sect. 6. II. PRESENT COMPRESSION SCHEMES There are different types of image compression techniques with their individual application area and advantages some of those are discusses as follows; A Embedded Zero tree Wavelet (EZW) The EZW compression algorithm was one of the first algorithms to explain the full power of wavelet-based image compression. It was introduced in the ground breaking paper of Shapiro. An EZW encoder is an encoder specifically designed to use with wavelet transforms. [1] The EZW encoder was originally designed to operate on images (2D-signals) but it can also be used on other dimensional signals. [2], [3] B  Set Partitioning In Herichirical Trees (SPIHT)       The SPIHT method is not a simple extension to the  traditional methods for image compression, it also represents an important advancement in the field. The basic principal is the same as progressive coding is applied, processing the image in order to a lowering threshold. The difference is in the idea of zero trees (spatial orientation trees in SPIHT). This is an idea that takes into consideration bounds between coeffIcients across subbands at different levels. [4] If there is a coeffIcient at the highest level of the transform in a particular subband which considered insignifIcant against a particular threshold, it is very probable that its descendants in lower levels will be insignifIcant too.[5] Therefore we can code fairly a large group of coeffIcients with one symbol.  The method provides the following:   Good image quality, High PSNR, principally for color images;  Progressive image transmission;  Produces a completely embedded coded file;  Uses simple quantization algorithm;   Lossless compression.  Exact bit rate or distortion possible;  Efficient combination with error protection. International Journal on Recent and Innovation Trends in Computing and Communication                                       ISSN: 2321-8169 Volume: 2 Issue: 7                                                                                                                                                                       2002 – 2006 _______________________________________________________________________________________________ 2003 IJRITCC | July 2014, Available @ http://www.ijritcc.org                                                                 _______________________________________________________________________________________  What makes SPIHT really excellent is that it yields all those individuality simultaneously.  C. Wavelet Difference Reduction (WDR)            The WDR compression scheme uses ROI concept. WDR introduced by Tian and Wells. It works as encodes the location of considerable wavelet transform values. The WDR gives better perceptual image quality than SPIHT and no searching through quad trees as in SPIHT but suits only for low resolution medical images at low bpp rate [6]. The WDR algorithm is less complex and provides better preservation of edges.  D.  Set Partitioned Embedded bloCK coder (SPECK)  The SPECK image compression scheme is different from some of the above mentioned schemes. It does not use code trees which span, and develop the similarity, across different sub bands; rather, it makes use of sets in the form of blocks. The main concept is to exploit the clustering of energy in frequency and space in hierarchical structures of transformed images. The SPECK algorithm belongs to the class of scalar quantized significance testing algorithm. It has its roots primarily in the concept developed in the SPIHT algorithm, and few block coding algorithms. III. IMPROVED SPIHT ALGORITHM The traditional SPIHT algorithm uses a spatial tree structure[7]. Improved SPIHT algorithm also works on same principal. The image wavelet coefficients encoded by SPIHT encoder to generate the bit stream. The bit stream again applied to Huffman encoder to generate the encoded data in the form of   1D string. Huffman coding requires less number of bits to represent the encoded data which increase the compression ratio to a great extend. A. Wavelet Transform Wavelet compression defines a way of analyzing an uncompressed image data in a recursive manner, resulting in a sequence of higher resolution images, each adding to the information content in lower resolution images. The major steps in wavelet compression are performing a Discrete Wavelet Transformation (DWT), quantization of the wavelet-space image sub bands information, and then encoding this information. Wavelet images are not compressed images; rather it is quantization and encoding block that performs the image compression and to store the compressed image. Image compression using wavelet innately results in a set of multi-resolution images; it is well suited to working with large imagery which needs to be selectively viewed at different resolution, as only the levels containing the required level of detail need to be decompressed. The Figure 1 shows the block diagram of wavelet based compression scheme.                Figure 1 Wavelet Transform based Image Compression  B. Flow Chart of Improved SPIHT Compression       Figure 2 Main Flow chart of Compression Process                     International Journal on Recent and Innovation Trends in Computing and Communication                                       ISSN: 2321-8169 Volume: 2 Issue: 7                                                                                                                                                                       2002 – 2006 _______________________________________________________________________________________________ 2004 IJRITCC | July 2014, Available @ http://www.ijritcc.org                                                                 _______________________________________________________________________________________ C. Decompression Process:  The decompression process reverses the compression process to produce the reconstructed image as shown in          Figure 5. The reconstructed image may have lost some information due to the compression, and may have an error or distortion compared to the original image; but this is not in the case of SPHIT. Almost all the image information reconstructed after the decompression process and the reconstructed image look similar to the original one.     V. RESULT         The above results tested on 512x512 Man and Chest medical image. We can apply any image size also; for compression/decompression speed measurement performance tested on Intel core 2 duo processor with 2GB RAM and 150GB HDD. TABLE I.  PERFORMANCE  OF TRADITIONAL SPIHT  ALGORITHM TABLE II PERFORMANCE  OF IMPROVED  SPIHT  ALGORITHM Figure 3 Main Flow Chart of Decompression Process  IV QUALITY MATRICES        The performance of the proposed scheme is gauged by different parameters. The visual reconstructed image quality is measured by the Peak Signal to Noise Ratio (PSNR). The correlation between the original image and the reconstructed image is monitored by the respective Mean Square Error (MSE) values of each.         Further the different original and extracted images are shown for different compression rate. The experimental result shows that the proposed algorithm for image compression gives better performance than the traditional SPIHT algorithm.                Table I and II shows the comparison of Traditional SPIHT with Huffman based Improved SPIHT algorithm image compression algorithm by varying the compression rate. The proposed algorithm well suited on medical images also. The performance speed of compression/decompression can be Image Traditional SPIHT Algorithm with Decomposition level=5 Comp Rate PSNR in dB CR Time Response for Compression Time Response for Decompression Man Image  512x512 0.1 23.9 16.72 1.87 Sec. 3.46 Sec 0.2 26.39 8.36 4.05 Sec. 4.30 Sec. 0.3 27.94 5.57 6.91 Sec. 6.61 Sec. 0.4 29.34 4.18 10.32 Sec. 9.15 Sec. 0.5 30.54 3.34 14.02 Sec. 12.51 Sec. 0.6 31.4 2.78 21.82 Sec. 16.81 Sec. Chest Medical Image 512x512 0.1 37.77 19.56 2.20 Sec. 3.03 Sec. 0.2 42.52 9.77 3.71 Sec. 3.89 Sec. 0.3 45.14 6.51 5.72 Sec. 5.54 Sec. 0.4 46.79 4.88 9.62 Sec. 8.80 Sec. 0.5 48.16 3.91 12.81 Sec. 10.98 Sec. 0.6 49.17 3.25 17.88 Sec. 16.14 Sec. Image Improved  SPIHT Algorithm with Decomposition level=5 Comp Rate PSNR in dB CR Time Response for Compression Time Response for Decompression Man Image  512x512 0.1 25.53 53.38 3.52 Sec. 3.59 Sec. 0.2 28.2 25.84 4.24 Sec. 5.20 Sec. 0.3 29.21 16.91 7.63 Sec. 8.95 Sec. 0.4 31.17 12.67 12.80 Sec. 14.36 Sec. 0.5 32.43 10.03 15.62 Sec. 16.54 Sec. 0.6 32.52 8.35 23.56 Sec. 24.57 Sec. Chest Medical Image 512x512 0.1 39.17 63.65 2.18 Sec. 3.30 Sec. 0.2 44.16 30.25 3.93 Sec. 4.52 Sec. 0.3 47.06 19.89 6.44 Sec. 6.67 Sec. 0.4 48.72 14.75 10.25 Sec. 10.74 Sec. 0.5 49.74 11.78 13.20 Sec. 13.52 Sec. 0.6 51.11 9.73 20.04 Sec. 19.02 Sec. International Journal on Recent and Innovation Trends in Computing and Communication                                       ISSN: 2321-8169 Volume: 2 Issue: 7                                                                                                                                                                       2002 – 2006 _______________________________________________________________________________________________ 2005 IJRITCC | July 2014, Available @ http://www.ijritcc.org                                                                 _______________________________________________________________________________________ enhanced by advance processer. Wavelet filter type used is   'bior4.4'.     Next proceeding section shows the performance measurement of Traditional SPIHT and Improved SPIHT algorithm. The Figure 6 shows the graphs of Compression Rate verses Compression Ratio and Compression rate verses PSNR value in db.                                               Figure 4 Original Medical Chest Image & Man 512x512 Image                         (a) Comp. rate= 0.1                                  (b) Comp. rate= 0.2                                                                                                a) Compression Rate Vs Compression Ratio                           (c) Comp. rate= 0.3       (d) Comp. rate= 0.4             (e) Comp. rate= 0.5       (f) Comp. rate= 0.6  Figure 5 Reconstructed Man Image by changing Compression rate from 0.1 to 0.6  The Figure 5 shows reconstructed images by changing the value of compression rate. It is observed that as rate of compression increases the value of PSNR also increases which results into better quality of reconstructed image. Change in compression rate also affect on compression ratio as compression rate increase compression ratio decreases further increases in compression rate results into more number of pixels required to represent the compressed image.                     b) Compression Rate Vs PSNR in db                             c) Compression Rate Vs Compression Ratio International Journal on Recent and Innovation Trends in Computing and Communication                                       ISSN: 2321-8169 Volume: 2 Issue: 7                                                                                                                                                                       2002 – 2006 _______________________________________________________________________________________________ 2006 IJRITCC | July 2014, Available @ http://www.ijritcc.org                                                                 _______________________________________________________________________________________    d) Compression Rate Vs PSNR in db     Figure 6 Traditional SPIHT and Improved SPIHT Results  VI. CONCLUSION  In this paper we are presented a Huffman coding based Improved SPIHT algorithm for image compression. The performance of Traditional SPIHT and Improved SPIHT algorithm is compared in terms of PSNR, Compression ratio, compression/decompression speed. The result shows that the improved approach enhances the quality parameters in all aspect and it gives almost three times greater performance in compression ratio than traditional SPIHT.                              REFERENCES [1] Jerome M Shapiro “Embedded Image coding using zero trees of wavelets coefficients”, IEEE Transactions on signal processing, Vol 41, no. 12, pp 3445-3462, Dec 1993. [2] Asad Islam & Pearlman, “An embedded and efficient low-complexity, hierarchical image coder”, Visual Comm. and Image processing, proceeding of SPIE, Vol 3 653, pp.294-305, January 1999. [3] James S Walker "Wavelet-Based Image Compression" The Transform and Data Compression Handbook, CRC Press LLC, 2001. [4] A. Abu-Hajar and R. Sankar, "Enhanced partial-SPIHT for lossless and lossy image compression," lCASSP 2003.  [5] Sanjay H. Dabhole, Virajit A. Gundale, Johan Potgieter   “An Efficient Modified Structure of CDF 9/7 Wavelet based on Adaptive Lifting with SPIHT for Lossy to Lossless Image Compression”. International Conference on Signal Processing, Image Processing and Pattern Recognition 2013. [6] R.Sudhakar, Ms R Karthiga, S. Jayaraman “Image Compression using Coding of Wavelet Coefficients – A Survey”,  ICGST-GVIP Journal, Volume (5), Issue (6),          pp 25-38 June 2005. [7]  Amir Said, William A. Pearlman, “A New, Fast       and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees”. IEEE   Transactions on Circuit and Systems for Video Technology, VOL. 6, No.3, pp 243-250, June 1996.               

Performance Measurement of Huffman Coding based Improved SPIHT Image compression AlgorithmÃ‚

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Performance Measurement of Huffman Coding based Improved SPIHT Image compression AlgorithmÃ‚

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International Journal on Recent and Innovation Trends in Computing and Communication