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Securable image compression using spiht algorithm



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  • 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 96 SECURABLE IMAGE COMPRESSION USING SPIHT ALGORITHM Sankaranarayanan S 1 , Deny.J 2 1 Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech Engineering College 2 Assistant Professor, Department of Electronics and Communication Engineering, Joe Suresh Engineering College ABSTRACT In multimedia application most of the images are in color. But color images contain lot of redundancy and require a large amount of storage space. For presenting the performance of different wavelet SPIHT algorithm is used for compression of color image. In this RGB component of color image are converted to Y, Cb and Cr before wavelet transform is applied in that Y is Luminance component while Cb and Cr are chrominance components of the image. Image is compressed for different bits per pixel by changing level of wavelet decomposition. For this simulation MATLab/SIMULINK software is used. Results are analyzed using peak signal to noise ratio (PSNR) and mean square error (MSE). Graphs are plotted to show the variation of PSNR for different bits per pixel and level of wavelet decomposition. Keywords: SPIHT, Color Image, Wavelet, luminance, chrominance. I. INTRODUCTION Color image compression is done by using component RGB. Color image contains lots of redundancy which will make it difficult to store and transmit. For the compression, a luminance - chrominance representation is considered due to superior to the RGB representation. This RGB images are transformed to one of the luminance-chrominance models, performing the compression process, and then transform to RGB models displays are most often provided output image direct RGB model. The luminance component represents the intensity of the image and looks like a grey scale version. The chrominance components represent the color information in the image. This paper represents the usage of wavelet transformation and SPIHT Algorithm for achieving high quality image that can be transmits and receives. INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August, 2013, pp. 96-100 © IAEME: Journal Impact Factor (2013): 5.8896 (Calculated by GISI) IJECET © I A E M E
  • 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 97 II. WAVELET TRANSFORMATION OF IMAGES Wavelet is a mathematical function that decomposes data into different frequency components, and then each component with a resolution matched to its scale. Wavelet takes advantages over Fourier methods for analysing physical situations of the signal contains discontinuities and sharp spikes. Wavelets were developed independently in the fields of Mathematics, Quantum Physics, Electrical Engineering, and Seismic Geology and have applied in various fields such as image compression, turbulence, human vision, radar, and earthquake prediction. The wavelet transform is identical to a hierarchical sub band filtering system, the sub bands are logarithmically spaced in frequency. The basic idea discrete wavelet transform (DWT) for two dimensional image .An image is first decomposed into four parts based on frequency of sub bands, critically sub sampling horizontal and vertical channels using sub bands named as low-low(LL), Low-High(LH), High-Low(HL), and High-High(HH). The sub band LL is further decomposed and critically sub sampled. This process is repeated several times, the resultant image is determined by the application at hand. The block diagram describing this process is shown in Figure 1. Each level has various bands information such as low-low (LL), Low-High (LH), High-Low (HL), and High-High (HH) frequency bands. Furthermore, from these DWT coefficients, the original image can be reconstructed. This reconstruction process is called the inverse DWT (IDWT). If C[m,n] represents an image, the DWT and IDWT for C[m,n] can similarly be defined by implementing the DWT and IDWT separately on each dimension. Figure 1. Method of Color Image Compression III. COLOR IMAGE COMPRESSION The input color image in RGB components is converted to the YCbCr components .The converted YCbCr Components as input to the Wavelet Transform to encoding the image and then compresses the YCbCr Components using SPIHT algorithm. The compressed YCbCr components then decompressed using SPIHT algorithm and the decompressed YCbCr components given to Inverse Wavelet Transform to decoding the image. The YCbCr Components converted into RGB components and original image as the output of Color Image Compression.
  • 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 98 IV. PROBLEM DEFINITION We need to reduce bandwidth of an image, with low mean square error, high peak signal to noise ratio and high accuracy. The complexity and low speed of compressing the image by EZW technique is the major problem while compression. V. PROPOSED SYSTEM The SPIHT algorithm uses set partitioning of hierarchical trees. Encryption and decryption improves the security while transmission. VI. MODULE DESCRIPTION The various modules are described as follows: • Conversion Color image into Grey image. • Discrete Wavelet transform (Encoding). • SPIHT Compression. • Inverse SPIHT Decompression. • Inverse discrete Wavelet transform (Decoding). • Conversion Grey image into Color image VII. SPIHT ALGORITHM The SPIHT image coding algorithm was developed in 1996 by Said and Pearlman and is another more efficient implementation of the embedded zero tree wavelet (EZW) algorithm by Shapiro. After the wavelet transform is applied to an image, the main algorithm works by partitioning the wavelet decomposed image into significant and insignificant partitions based on the following function: Sn(T) = {1, max(i,j)eT {|Ci,j|} >= 2n } 0, otherwise Where Sn(T), is the significance of a set of co-ordinates T, and Ci,j is the coefficient value at co-ordinate (i,j) . There are two passes in the algorithm - the sorting pass and the refinement pass. The sorting pass is performed on the list of insignificant sets (LIS), list of insignificant pixels (LIP) and the list of significant pixels (LSP). The LIP and LSP consist of nodes that contain single pixels, while the LIS contains nodes that have descendants. The maximum number of bits required to represent the largest coefficient in the spatial orientation tree is obtained and designated as nmax, which is given by, n max=[log2(maxi,j{|ci,j|})] During the sorting pass, those co-ordinates of the pixels which remain in the LIP are tested for significance by using above equation. The result, Sn(T), is sent to the output. Those that are significant will be transferred to the LSP as well as having their sign bit output. Sets in the LIS (which consists of nodes with descendants will also have their significance tested and, if found to be significant, will be removed and partitioned into subsets. Subsets with a single coefficient and found to be significant will be added to the LSP, or else they will be added to the LIP.
  • 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 99 During the refinement pass, the nth most significant bit of the coefficients in the LSP is output. The value of n is decreased by 1 and the sorting and refinement passes are repeated. This continues until either the desired rate is reached or n =0, and all the nodes in the LSP have all their bits output. The latter case will result in almost perfect reconstruction as all the coefficients are processed completely. The bit rate can be controlled precisely in the SPIHT algorithm because the output produced is in single bits and the algorithm can be terminated at any time. The decoding process follows the encoding exactly and is almost symmetrical in terms of processing time. VIII. MODELLING AND RESULTS Color image compression is very important in today’s communication era because most of the images are in color. Color images take more space for storage. Also without compression it may take long time for transferring images through internet. MATLab/SIMULINK software is used for simulating this work. In our analysis we have used true color image (RGB 24 bit). Image is converted to YCbCr format. YCbCr or Y′CbCr, sometimes written YCBCR or Y′CBCR, is a family of color spaces used as a part of the color image pipeline in video and digital photography systems. Y′ is the luma component and CB and CR are the blue-difference and red-difference chroma components. Y′ (with prime) is distinguished from Y which is luminance, meaning that light intensity is non-linearly encoded using gamma correction. YCbCr image. After converting wavelet analysis is done for Y, Cb, Cr. Then the data is compressed using SPIHT algorithm. Lena image shown below is used for analysis. For calculating PSNR only Y (Luminance) component of original and reconstructed image is used. Image shown in Figure 2 is used for our analysis. Following are the result for different wavelets. Figure 2. RGB Image IX. CONCLUSIONS Compressing color images efficiently are one of the main problems in multimedia applications. So we have tested the efficiency of color image compression using SPIHT algorithm. The SPIHT algorithm is applied for luminance (Y) and chrominance (Cb, Cr) part of RGB to YCbCr transformed image. Reconstructed image is verified using human vision and PSNR. Huffman and arithmetic coding can be added to increase the compression. Transmission while using encryption and decryption is applicable for security purpose. We can test the channel behavior by sending compressed image between two computer and check the reconstructed image.
  • 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 100 REFERENCES [1] Said A, Pearlman WA. “A new fast and efficient image codec based on set partitioning in hierarchical trees”. IEEE Transactions on Circuits and Systems for Video Technology 1996;6:243–50. [2] J M Shapiro, "Embedded image coding using Zero trees of wavelet coefficients", IEEE Trans. Signal Processing, Vol 41, pp 3445-3462, Dec 1993. [3] Rafael C. Gonzalez and Richard E. Woods. Digital Image Processing. Pearson Education, Englewood Cliffs, 2002 . [4] K.Sayood, “Introduction to Data Compression”, 2nd edition, Academic Press, Morgan Kaufman Publishers, 2000. [5] G.Sadashivappa, K.V.S.AnandaBabu, "Performance analysis of Image Coding of Wavelets" IJCSNS International Journal of Computer Science and Network Security, Oct 2008. [6] G.Sadashivappa, K.V.S.AnandaBabu, “Wavelet Filters for Image Compression, an analytical study” ICGST-GVIP Journal, Volume 9, Issue 5, September 2009. [7] K.P.Soman,K.I.Ramachandran “Insight into Wavelets from theory to practice”. Prentice-Hall of India Private Limited. [8] EZW present at Website: http://pagesperso-orangefr/polyvalens/Clemens/ezw/ ezw .html [9] ANTONINI, M., BARLAUD, M., MATHIEU, P., and DAUBECHIES, I.: ‘Image coding using wavelet transform’, IEEE Trans. Image Process., 1992,1,(2),pp.205-220. [10] Shipra Gupta and Chirag Sharma, “A Novel Technique in SPIHT for Medical Image Compression”, International Journal of Graphics and Multimedia (IJGM), Volume 4, Issue 1, 2013, pp. 1 - 8, ISSN Print: 0976 – 6448, ISSN Online: 0976 –6456. [11] P. Prasanth Babu, L.Rangaiah and D.Maruthi Kumar, “Comparison and Improvement of Image Compression using DCT, DWT & Huffman Encoding Techniques”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 54 - 60, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [12] Pardeep Singh, Nivedita and Sugandha Sharma, “A Comparative Study: Block Truncation Coding, Wavelet, Embedded Zerotree and Fractal Image Compression on Color Image”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 2, 2012, pp. 10 - 21, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [13] Hitashi and Sugandha Sharma, “Fractal Image Compression Scheme using Biogeography Based Optimization on Color Images”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 35 - 46, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.