Dr.Hazeem Al-Khafaji Dept. of Computer Science, Thi-Qar University, College of Science, Iraq

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1 Volume 4 Issue 6 June 014 ISSN: 77 18X International Journal of Advaned Researh in Computer Siene and Software Engineering Researh Paper Available online at: Medial Image Compression using Wavelet Quadrants of Polynomial Predition Coding & Bit Plane Sliing Dr. Ghadah Al-Khafaji Dept. of Computer Siene Baghdad University College of Siene Iraq Dr.Hazeem Al-Khafaji Dept. of Computer Siene Thi-Qar University College of Siene Iraq Abstrat--In this paper a promising medial ompression is introdued it is based on utilizing the wavelet sub bands aording to orrelation embedded using the linear polynomial approximation model and bit plane sliing. The test results showed elegant performane of lossless ompression tehniques. Keywords-Image Compression Medial Image Wavelet Transform. I. INTRODUCTION Medial ompression or simply refereed as lossless ompression haraterized by preserving quality; where the an be reonstruted exatly as idential to the original with error free but unfortunately there is a limitation of the ompression performane ahieved (i.e. small ompression ratio from to 10) due to on exploiting the statistial redundany only (i.e. exploits oding redundany and/or inter pixel redundan. Reviews of various medial ompression tehniques an be found in [1-8]. In order to improve the performane of the medial ompression system either by utilizing the ombination different tehniques suh as wavelet and predition [9-11] or by exploring a tehnique that selets signifiant bloks and exlude others [1].This paper introdued an effetive ombination tehniques that uses the polynomial preditive oding of linear based with the bit plane sliing aording to the orrelation embedding between the wavelet quadrants. The rest of this paper is organized as follows; the proposed medial ompression system with the experimental results given in setions and 3 respetively. II. THE PROPOSED SYSTEM The steps below illustrate learly the implementation of proposed system in details the system layout shown in Figure(1): Step 1: Load the original unompressed gray I of size N N. Step : Apply the wavelet transform that haraterized by simpliity and high ompression ratio. The transform basially based on deompose input I into four quadrants eah of size (N/ N/) that omposed of approximation subband () and detail sub bands ( and ). In general the approximation sub band () onsidered the most signifiantly important part sine ontains all the information while the other sub bands or details sub bands onsidered to be less signifiant sine ontains very small information and they an be set to zero without signifiantly hanging the [1314]. Step 3: Compute autoorrelation funtionbetween neighbouring pixels of approximation and detail sub bands suh as: ( N / ) 1 ( N / ) 1 x 0 ( N / ) 1 ( N / ) 1 x 0 ( N / ) 1 ( N / ) 1 x 0 ( N / ) 1 ( N / ) 1 x 0 y 0 y 0 y 0 (( ( ).(( ( x x y 0 ( N / ) 1 ( N / ) 1 x 0 (( ( ).(( ( x x ( N / ) 1 ( N / ) 1 x 0 (( ( ).(( ( x x ( N / ) 1 ( N / ) 1 x 0 (( ( ( N / ) 1 ( N / ) 1 x 0 y 0 y 0 (( ( ) y 0 (( ( ) (( ( ) y 0 ).(( ( x x (( ( 014 IJARCSSE All Rights Reserved Page 3 y y y y y y y y ) ) ) ) ) ) ) ) ) (1) () (3) (4)

2 Ghadah et al. International Journal of Advaned Researh in Computer Siene and Software Engineering 4(6) June pp Here m m m and m refers to the mean of the approximation and details sub ands (x+xy+y) (x+xy+y) (x+xy+y) and (x+xy+y) the shifted s by and refers to the amount of shift in pixel(s) in both diretions. Step 4: Apply ompression tehniques depending on the orrelation embedded between quadrants in other words apply polynomial predition oding or bit plane sliing of the subband s suh that: a) For highly orrelated quadrant namely the approximation subband () the polynomial predition of linear based model utilized to remove the orrelation or spatial redundany embedded between pixel values using the following steps: 1- Partition the approximation subband () into nonoverlapping bloks of fixed size n n and performs the polynomial representation aording to equations (56 and 7) [15]: n 1n1 1 a 0 (......(5) n n i0 j0 n1 n1 ( ( j x ) i0 j0 a1...( 6) n1 n1 ( j x ) i0 j0 n1n1 ( ( i y ) i0 j0 a...( 7) n1n1 ( i y ) i0 j0 Where ( is the approximation sub-band of original of blok of size (n n) and n 1 x y......( 8) - Create the predited L L using the alulated oeffiients above suh as: a0 a1 ( j x) a( i y)...(9) 3-Find the residual or residue between the original and predited L L approximated subband s R( ( (...( 10) b) For less orrelated quadrants or the detail sub bands ( and ) the simple plane sliing tehniques exploited the following steps performed for eah detail band: 1- Apply mapping proess by onverting the negative and positive values into positive values only either even or odd using the mapping formula below. Mapi ifmapi 0 Mapi...( 11) Mapi 1 else Where Map i is the i th value of the subband where the negative values mapped to odd while the positive values mapped to even. - Convert eah of the mapped detail sub bands into it s layers aording to intensity value where the bit plane sliing separating it into eight layers in general the Least Signifiant Layers (LSLs) arranged from layer 0 to layer 3 while the Most Signifiant Layers (MSLs) from layer 4 to layer Remove the low or small ontribution effets by disarding the Least Signifiant Layers (LSLs) and keeping only the Most Signifiant Layers (MSLs) of highly effet. Simply now eah subband needs or required only four layers. Step 5: Use LZW symbol enoder of ditionary based to ompress the residual oeffiients of approximated subband (i.e. ) and the most signifiant layers (MSLs) of the mapped detail sub bands. Step 6: Reonstrut the ompressed that idential to the original one I using the following steps: a) Use the symbol deoder to reonstrut the ompressed information. b) For the approximation subband the residual along with the oeffiients used to rebuild the quadrant: ( R( (...( 1) ) For the detail sub bands applied the following steps: 1- Use the Most Signifiant Layers (MSLs) of eah subband the four high order layers. - Perform the inverse mapping proess to map eah value into equivalent representation by applying the following: Mapi / if even InvMapi...( 13) ( Mapi 1) / else Here the values mapped again into negative and positive values. d) Apply the inverse wavelet transform to reonstrut the ompressed. 014 IJARCSSE All Rights Reserved Page 33

3 Ghadah et al. International Journal of Advaned Researh in Computer Siene and Software Engineering 4(6) June pp III. EXPERIMENTS AND RESULTS In general to test the system performane various medial types adopted (see Figure for an over view) where all the s are gray square of size pixels with blok sizes {4 4 and 8 8} using the only measure of goodness orresponding to ompression ratio. Figure 3 shows the autoorrelation funtion of the wavelet transform sub bands of the tested s that presents learly the spatial redundany within the quadrants of approximation and subband details. The results are shown in Table 1 that summarizes the size of the ompressed information and the ompression ratio against the utilized blok sizes for the test s. The highly superior ompression ratio ahieved for a lossless medial system haraterizes this tehniques ompared to other tehniques based on the same onept [ ] in whih the ompression ratio improved about three times or more on average. Also the result illustrates that the ompression ratio vary aording to details or harateristis where for simple or low detail s like tummy and hest x-ray higher ompression ahieved ompared to omplex or highly detail s like brain knee t-san and eho. Lastly it is obvious that the ompression ratio of the proposed system is affeted by the blok size of the approximation subband (i.e. ) whereas the blok size inrease the ompression ratio improves beause less oeffiient parameters required. Original Image I For the use the polynomial oding layer7.. layer1 Use the oeffiients to reate the predited and find the residual Use only the most signifiant layers from layer3 to layer7 LZW Enoder Apply DWT on the Find the autoorrelation funtion for sub bands layer0 For the and use the Bit Plane Sliing Compressed or deoded Image idential to original I Use the oeffiients to reate the predited and add it to the residual to reonstrut the Use the most signifiant layers to reonstrut the detail sub bands LZW Deoder Apply IDWT to reonstrut the deoded Fig. (1): Compression system struture. TABLE 1: THE MEDICAL COMPRESSION PERFORMANCE FOR THE TESTED IMAGES Tested Image Size in bytes of Original Blok Size 4 Blok Size 8 Size in bytes ompressed information Comp. Ratio Size in bytes ompressed information Comp. Ratio Brain Eho Knee Tummy Xray-hest IJARCSSE All Rights Reserved Page 34

4 Ghadah et al. International Journal of Advaned Researh in Computer Siene and Software Engineering 4(6) June pp a b d e Fig. (): Overview of the medial test s. a b d e Fig. (3): The autoorrelation values of approximation and detail sub bands of the tested s. REFERENCES [1] Bramble J. M. Huang H. K. and Murphy M. D Image Data Compression. Radiology [] David K. and Shulman H An Overview of Digital Compression of Medial Images: Can We Use Lossy Image Compression in Radiology? Canadian Assoiation of Radiologists Journal 57(4) [3] Ferni Ukrit M. Umamageswar A. and Suresh G.R A Survey on Lossless Compression for Medial Images. International Journal of Computer Appliations (IJCA) 31(8) [4] Sepehrband F. Mortazav M. Gorsh S. and Choupan J Simple Lossless and Near-Lossless Medial Image Compression Based on Enhaned DPCM Transformation. Communiations Computers and Signal Proessing (PaRim) IEEE Conferene [5] Sridev S. Vijayakuymar V. and Anuja R. 01. A Survey on Various Compression Methods for Medial Images. International Journal of Intelligent Systems and Appliations IJARCSSE All Rights Reserved Page 35

5 Ghadah et al. International Journal of Advaned Researh in Computer Siene and Software Engineering 4(6) June pp [6] Alagendran B. and Manimurugan S. 01. A Survey on Various Medial Image Compression Tehniques. International Journal of Soft Computing and Engineering (IJSCE) (1) [7] Kesavamurthy T. and Thiyagarajan K Lossless Color Medial Image Compression Using Adaptive Blok- Based Enoding for Human Computed Topographi Images. International Journal of Imaging System and Tehnology 3(3) [8] Venkatraman B. Vaithiyanathan V. and Karthikeyan B Review on Lossless Image Compression Tehniques for Welding Radiographi Images. Researh Journal of Applied Sienes Engineering and Tehnology 5(1) [9] Das M. and Burgett S Lossless Compression of Medial Images Using Two Dimensional Multipliative Autoregressive Models. IEEE Transations on Medial Imaging 1(4) [10] Ghadah Al-K. and Haider Al-M Lossless Compression of Medial Images using Multiresolution Polynomial Approximation Model. International Journal of Computer Appliations 76(3)38-4. [11] Ghadah Al-K Wavelet Transform and Polynomial Approximation Model for Lossless Medial Image Compression. International Journal of Advaned Researh Computer Siene and Software Engineering 4(3) [1] Ghadah Al-K. and George L. E..013.Fast Lossless Compression of Medial Images based on Polynomial. International Journal of Computer Appliations 70(15) 8-3. [13] Sayood K Introdution to Data Compression nd ed. Aademi Press Morgan Kaufman Publishers. [14] Tsa M. and Hung H DCT and DWT based Image Watermarking Using Sub sampling. in Pro. Of the 005 IEEE Fourth Int. Conf. on Mahine Learning and Cybernetis China 005. [15] George L. E. and Sultan B Image Compression Based on Wavelet Polynomial and Quadtree. Journal of Applied Computer Siene & Mathematis 11(5) [16] Ghadah Al-K Image Compression based on Quadtree and Polynomial. International Journal of Computer Appliations 76(3) IJARCSSE All Rights Reserved Page 36