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20120140504016

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 146 COLOUR IMAGE COMPRESSION USING BLOCK TRUNCATION CODING AND GENETIC ALGORITHM T.M. Amarunnishad1 , Meekha Merina George2 1 Dept. of Comp. Sci. & Engg., TKM College of Engg., Kollam - 691005, Kerala, India. 2 Dept. of Comp. Sci. & Engg., TKM Inst. of Tech., Kollam - 691505, Kerala, India. ABSTRACT In this paper a colour image data compression method using block truncation coding (BTC) and genetic Algorithm (GA) is presented. This compression technique reduces the computational complexity and provides acceptable visual quality reconstructed images. BTC acts as the basic compression technique but it exhibits two disadvantages such as the false contour and blocking effect. Hence error diffused block truncation coding (EDBTC) is used to overcome the two issues. The proposed system is made applicable for colour images. Colour images comprise three planes: red, green, and blue. There are very high correlations between the images in these planes. This motivates the use of one common bit plane to represent all three colour bit planes in the proposed compression method for colour images. GA is used to make the common bit plane optimal. Experimental results show that the proposed method provides acceptable visual quality reconstructed images with high peak signal to noise ratio (PSNR) when compared to the conventional BTC and EDBTC methods. Keywords: Image Compression, Block Truncation Coding, Genetic Algorithm, Optimal Bit Plane, Reconstructed Image, Peak Signal To Noise Ratio. 1. INTRODUCTION Internet has become a very popular means of communication due the emergence and flourishing of network technologies. In recent years, the development and demand of multimedia product grows increasingly fast, contributing to insufficient bandwidth of network and storage of memory device. Therefore, the theory of data compression becomes more and more significant for INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  2. 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 147 reducing the data redundancy to save more hardware space and transmission bandwidth. Images form a significant part of data, its use in the internet continue to grow. This necessitates the need for storing and transmitting huge amount of data. The purpose of image compression is to represent images with less data in order to save storage space or transmission time. The image compression techniques are categorized into two main classifications namely lossy compression techniques and Lossless compression techniques [1]. Lossy data compression is named for what it does. After one applies lossy data compression to a message, the message can never be recovered exactly as it was before when compressed. When the compressed message is decoded it does not give back the original message. As lossy compression cannot be decoded to yield the exact original message, it is not a good method of compression for critical data, such as textual data. It is most useful for Digitally Sampled Analog Data (DSAD) as it consists mostly of sound, video, graphics, or picture files. Lossless data compression is also named for what it does. In a lossless data compression file the original message can be exactly decoded. Lossless data compression works by finding repeated patterns in a message and encoding those patterns in an efficient manner. For this reason, lossless data compression is also referred to as redundancy reduction. Because redundancy reduction is dependent on patterns in the message; it does not work well on random messages. Lossless data compression is ideal for text [2]. We propose a colour image compression method utilizing the basic conventional block truncation coding (BTC) method. This method has demonstrated in producing perceptually high quality reconstructed images with high peak signal to noise ratio (PSNR) values. The BTC is a fast, simple algorithm used for coding images at good quality and at low to moderate compression ratios. The basic algorithm of BTC was developed by Delp and Mitchel [3] by incorporating ideas relative to exploiting statistical moments in the context of image compression. Although the performance of BTC is inferior to transform coding such as JPEG [4], it requires a significantly smaller computational load and has gained popularity due to its practical usefulness for low cost applications such as software based multimedia systems. Since its introduction, the BTC algorithm has been modified in various ways and several different basic algorithms under the generic name BTC have been appeared in literature [5] – [8]. All these BTC algorithms are simple, computationally fast and capable in fine edge preservation. However, due to insufficient quantization levels, it produces ragged edges in the reconstructed images. Another problem of BTC is its high bit rate. For a fixed block size BTC, the bit rate is about 2 bits/pixel. There are BTC algorithms [9] – [10], which can obtain a lower bit rate. However, the computation is complex so that it is hard to implement in real time processing. In this paper a BTC scheme, error diffused BTC (EDBTC) [11] is used as the basic compression technique because of the fact that it overcomes the issues in conventional BTC [3]. This compression technique is used for colour images by separating each pixel into R, G, and B planes [12]. Genetic algorithm (GA) [13] is used to optimize the bit plane thus improving the visual quality of the reconstructed image with a better PSNR values and reduced error. The rest of the paper is organized as follows. Section 2 describes the BTC. The proposed method is discussed in section 3. The results are illustrated in section 4, and section 5 concludes this paper. 2. BLOCK TRUNCATION CODING The BTC, a type of lossy image compression technique is a two level non-parametric binary encoder based on moment preserving quantization that adapts to the local properties of the image. The input image is partitioned in to non-overlapping pixel blocks of size n × n, and each block is processed individually. In this process, the first moment (‫ݔ‬ҧ), second-moment (‫ݔ‬ଶതതത), and variance (ߪଶ )
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 148 of the input block are calculated to yield the mean, high-mean and low-mean for further BTC processing. The equations to compute the above are given below: ‫ݔ‬ҧ ൌ ଵ ௠ ∑ ‫ݔ‬௜ ௠ ௜ୀଵ (1) ‫ݔ‬ଶതതത ൌ ଵ ௠ ∑ ‫ݔ‬௜ ଶ௠ ௜ୀଵ (2) ߪଶ ൌ ‫ݔ‬ଶ െ ሺ‫ݔ‬ሻଶ (3) Where m = n × n (say n = 4) and xi = x1 , x2 , …. , xm be the pixels in a block of the input image. The BTC algorithm preserves the first two sample moments of each pixel block, when the original block is substituted by its quantization levels. That is, every pixel in a block is represented by either a high-mean or a low-mean of the block. Thus, the following two conditions are maintained: ݉‫ݔ‬ҧ ൌ ሺ݉ െ ‫ݍ‬ሻܽ ൅ ‫ܾݍ‬ (4) ݉‫ݔ‬ଶതതത ൌ ሺ݉ െ ‫ݍ‬ሻܽଶ ൅ ‫ܾݍ‬ଶ (5) where m = n × n, and q denotes the number of pixels with values greater than ‫ݔ‬ҧ. The high-mean and low-mean used to represent a block can be evaluated as follows: ܽ ൌ ‫ݔ‬ഥ– ߪ ට ௤ ௠ି௤ (6) ܾ ൌ ‫ݔ‬ഥ ൅ ߪ ට ௠ି௤ ௤ (7) where the two variables ‘a’ and ‘b’ denote the low-mean and high-mean, respectively and ߪ is the standard deviation. The bit plane with values ‘0s’ and ‘1s’ is generated by the one-bit quantizer, which is used to record the distribution of the two quantization levels, low-mean and high-mean defined by ‫,ݐݑ݌ݐݑ݋‬ ‫ݕ‬௜ ൌ ൜ ܾ ൌ 1, ݂݅ ‫ݔ‬௜ ൒ ‫ݔ‬௧௛ ܽ ൌ 0, ݂݅ ‫ݔ‬௜ ൏ ‫ݔ‬௧௛ (8) Where ‫ݔ‬௧௛ is the threshold and is taken as the mean of the block ‫ݔ‬ҧ The compressed image data contains mean, ‫ݔ‬ҧ and the standard deviation, ߪ of all the 4 × 4 blocks and the bit plane. Each input image is transmitted as bit plane consisting of ‘0s’ and ‘1s’ along with quantized information on the ‫ݔ‬ҧ and ߪ . At the receiver end, each image block is reconstructed such that ‫ݔ‬ҧ and ߪ are preserved. For each pixel with value xi , the output levels ‘a’ and ‘b’ are calculated using equations (6) and (7) and each image block is reconstructed by assigning these values to pixels in accordance with ‘0s’ and ‘1s’ in the bit plane. In the bit planes there can be blocks with all the values either ‘0’ or ‘1’, such blocks are visually continuous indicating no edges in them. The sample mean, ‫ݔ‬ҧ is used to represent that block. So while reconstructing the image, such blocks are given a reconstruction value equal to the mean, ‫ݔ‬ҧ.
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 149 Figure 1 illustrates an example of the BTC compression. The block size is assumed to be 4 × 4 pixels. The computed ‫ݔ‬ and ߪ values are 191 and 19, respectively. A bit ‘1’ is assigned to all pixels with values greater than the mean and bit ‘0’ is assigned to pixels with values less than the mean. Low-mean and high-mean are 183 and 206, respectively. The image is reconstructed by replacing the bit ‘1s’ in the block with high-mean and bit ‘0s’ with low-mean. ‫;191=ݔ‬ ߪ =19 206 212 189 177 1 1 0 0 206 206 183 183 213 194 182 184 1 1 0 0 206 206 183 183 192 179 187 178 1 0 0 0 206 183 183 183 179 186 221 185 0 0 1 0 183 183 206 183 (a)Input image block (b) transmitted data (c) output image block Fig. 1: Example of BTC compression for gray scale image 3. PROPOSED METHOD For colour image compression, three bit planes are obtained for R, G and B planes from the input colour image. Since there is a high correlation between the images in these planes, a common bit plane is used to represent all three colour bit planes. The implementation of the method is depicted in Fig 2. First the colour image is decomposed into the respective R, G, and B planes. Each input colour plane is divided into 4 × 4 non-overlapping blocks of pixels and the block mean and standard deviation are calculated. The average of the means of the three R, G, and B blocks is calculated to use in the one-bit quantizer for the common bit plane generation. If the average of three pixels of the respective R, G, and B planes is greater than the average mean value, the pixel in the common bit plane is represented as bit ‘1’ otherwise ‘0’. At the receiving end, the high-mean and low-mean are calculated for the R, G, and B planes separately by using the respective mean and standard deviation. During reconstruction of the image, bit ‘1s’ in the bit plane are replaced by the corresponding high-mean and bit ‘0s’ are replaced by the low-mean.
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 150 Fig. 2: Schematic of Colour image BTC Decompose the image into R,G,B planes G plane: calculate the mean and std. (‫ݔ‬ீ,ߪீ) B plane: calculate the mean and std. (‫ݔ‬஻,ߪ஻) R plane: calculate the mean and std. (‫ݔ‬ோ,ߪோ) Calculate the average of means: ‫ݔ‬ோ , ‫ݔ‬ீ , ‫ݔ‬஻ Find the common bit plane Transmit common bit plane and mean and std. of R, G, B planes At the receiver find ‘q’, the no. of pixels in the bit plane with value ‘1’ ‫ݔ‬ோ ‫ݔ‬஻ ‫ݔ‬ீ ‫ݔ‬ீ,ߪீ ‫ݔ‬஻,ߪ஻ ‫ݔ‬ோ,ߪோ Bit plane values all 0/1? Reconstruct the pixels for the R,G,B planes by the respective means of the block Reconstruct the pixels by using equations (6) and (7) No Yes Reconstructed image Input image
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 151 3.1 Error Diffused Block Truncation Coding The traditional BTC comes with two issues: false contour effect and blocking effect. To reduce with these issues EDBTC [11] was used. The EDBTC exploits the inherent dithering property of the error diffusion to overcome false contour problem. Moreover, the blocking effect can also be eased by its error kernel, since both sides of a boundary between any pair of resulting image blocks being correlated. ‫ݒ‬௜,௝ ൌ ‫ݔ‬௜,௝ ൅ ‫ݔ‬௜,௝ ′ (9) ‫݁ݎ݄݁ݓ‬ ‫ݔ‬௜,௝ ′ ൌ ∑ e୧ା୫, ୨ା୬ ൈ ek୫,୬ሺ୫,୬ሻ ݁௜,௝ ൌ ‫ݒ‬௜,௝ െ ‫ݕ‬௜,௝ (10) ‫݁ݎ݄݁ݓ‬ ‫ݕ‬௜,௝ ൌ ൜ ‫ݔ‬௠௔௫, ݂݅ ݄௜,௝ ൌ 1 ‫ݔ‬௠௜௡, ݂݅ ݄௜,௝ ൌ 0 ܽ݊݀ ݄௜,௝ ൌ ቊ 1, ݂݅ ‫ݒ‬௜,௝ ൒ ‫ݔ‬ 0, ݂݅ ‫ݒ‬௜,௝ ൏ ‫ݔ‬ The variables ‫ݔ‬௜,௝ є X and ‫ݕ‬௜,௝ є Y denote the input pixel value and the EDBTC output, respectively. The output ‫ݕ‬௜,௝ is substituted by either maximum ሺ‫ݔ‬୫ୟ୶ ሻ or minimum (‫ݔ‬௠௜௡) of the block according to the bitmap ݄௜,௝ є H. The variable ek୫,୬ denotes the employed error kernel, which is used to diffuse the error ݁௜,௝ to its neighboring pixels. Herein, the Floyd- Steinberg’s error kernel [14] as shown in Fig 3 was employed, where the notation x denotes the current processing position. The error at the boundary of a block should be diffused to its neighbouring blocks, thus the blocking effect can be significantly eased. An example is illustrated in Fig 4. The block size is assumed to be 4 × 4 pixels. The mean of the block ‫ݔ‬ҧ is 191. The minimum pixel value and maximum pixel value are 177 and 221, respectively. A bit 1 is assigned to all pixels with values greater than mean and bit 0 is assigned to pixels with values less than the mean. The image is reconstructed by replacing the bit 1’s in the block with 221 and bit 0’s with 177. The neighbouring pixels in the reconstructed image are constructed using Floyd’s error kernel. An error is computed by taking the difference between original pixel value and the corresponding reconstructed pixel value i.e 205 – 221 which is -16. Using Floyd’s error kernel the pixel value 205 is calculated (212 + (7/16) x (-16) = 205). The EDBTC compression method can be applied directly to colour images. x 7/16 3/16 5/16 1/16 Fig. 3: Floyd’s error kerne
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 152 YES NO 205 212 189 177 221 205 189 177 213 194 182 184 208 193 182 184 192 179 187 178 192 179 187 178 179 186 221 185 179 186 221 185 Gray scale image Reconstructed image Fig. 4: Example of EDBTC 3.2 Genetic Algorithm It is a fact that stronger individuals are likely to win a competition; GA utilizes this fact. GAs are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions one might not otherwise find in a lifetime. Flow chart of a typical genetic algorithm [12] is shown in Fig 5. Fig. 5: Flowchart of genetic algorithm GA is based on Darwin’s theory of natural selection and it uses the survival of the fitness strategy to find the optimal starting point from a population of candidate points. It uses a population of possible solutions to the search space. Each solution is encoded in a string called a chromosome Population of chromosomes Selection Crossover Mutation StopTerminal condition
  8. 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 153 (or genome). Chromosomes are evaluated for fitness on each generation (iteration); chromosomes that are more fit have a high probability of surviving. Once the surviving population is chosen, different “parent” chromosomes are combined to form “child” chromosomes. Chromosomes may undergo mutation. A new generation is formed, the process is repeated. By selection, cross-over, and mutation, GAs searches the solution space while creating stronger solutions over each generation. Selection A chromosome is presented as a combination of ‘1s’ or ‘0s’. The initial population of N chromosomes is randomly generated. It is possible to preprocess the chromosomes to increase the convergence rate of GAs with a better initialization. A subset of the population forms the mating pool. Crossover Once the mating pool has been formed, genetic information between the 2 bit planes is exchanged to create a new population. This process of exchange of information is the crossover process. It is illustrated in Fig 6. Parent 1 Parent 2 Child 1 Child 2 1 0 0 1 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 Exchanging tails Fig. 6: Illustration of Crossover Mutation Parent selection and crossover operation may result in somewhat identical individuals who may not be the fittest. Thus the genetic algorithm can get stuck after sometime. New genetic information is brought into population by simulating the process of mutation. Mutation is the random operation for changing the fitness and is used sparingly. The mutation operator changes ‘1’ to ‘0’ and ‘0’ to ‘1’ with a small mutation probability ‘Pm’. It is illustrated in Fig 7.
  9. 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 154 Child 1 Child 2 1 0 0 1 1 1 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 1 0 Mutated Fig. 7: Illustration of Mutation Fitness function The fitness function basically determines which possible solutions get passed on to multiply and mutate into the next generation of solutions. This is usually done by analyzing the "genes," which hold some data about a particular solution to the problem you are trying to solve. The fitness function will look at the genes and make some qualitative assessment, returning a fitness value for that solution. The rest of the genetic algorithm will discard any solutions with a "poor" fitness value and accept any with a "good" fitness value. In short: the goal of a fitness function is to provide a meaningful, measurable, and comparable value given a set of genes. Two fitness function f and pi are defined to calculate the fitness value: ݂ ൌ ଵ ெௌா (11) ‫݌‬ ௜ ೑ሺ೒೔ሻ ∑ ೑ሺ೒೔ሻೖಿ ೕసభ ݇ (12) where MSE is the mean square error; k is kth iteration; gi is the preserved gene; N is the amount of preserved chromosomes in the mating pool ‫ܧܵܯ‬ ൌ ଵ ெൈே ∑ ∑ ሾ‫ܫ‬ሺ݅, ݆ሻ െ ‫ܫ‬′ሺ݅, ݆ሻሿଶே ௝ୀଵ ெ ௜ୀଵ (13) where I is the input image and I’ is the output image 4. RESULTS AND DISCUSSIONS In this section, simulation results are illustrated to demonstrate the effectiveness of the proposed method. The performance of the proposed system has been tested with a set of gray scale and colour images, ten each, and each of size 256 × 256. For comparison, BTC [3] and EDBTC [11] are also implemented. The commonly used measures in a variety of image coding system, mean
  10. 10. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 155 absolute error (MAE), root mean square error (RMSE), and PSNR have been used to evaluate the performance of the proposed method. They are defined as follows: ‫ܧܣܯ‬ ൌ ଵ ௑௒ ∑ ∑ ሾ|‫ܫ‬ሺ‫,ݔ‬ ‫ݕ‬ሻ െ ‫ܫ‬′ሺ‫,ݔ‬ ‫ݕ‬ሻ|ሿ ௬ ௝ୀଵ ௫ ௜ୀଵ (14) ܴ‫ܧܵܯ‬ ൌ ሾ ଵ ௑௒ ∑ ∑ ሾ‫ܫ‬ሺ‫,ݔ‬ ‫ݕ‬ሻ െ ሺ‫,ݔ‬ ‫ݕ‬ሻሿଶ ሿଵ/ଶ௬ ௝ୀଵ ௫ ௜ୀଵ (15) ܴܲܵܰሺ݀‫ܤ‬ሻ ൌ 10݈‫݃݋‬ଵ଴ ൤ ௠௔௫ೣ,೤ூሺ௫,௬ሻ ோெௌா ൨ ଶ (16) Where Iሺx, yሻ is the original image, I′(x,y) is the reconstructed image, and X×Y is the dimension of the images. A lower value of RMSE means lesser error in the reconstruction, and as seen from the inverse relation between the RMSE and PSNR, this translates to a high value of PSNR. Logically, a higher value of PSNR is preferable because of the higher signal to noise ratio. Here, the signal is the original image and the noise is the error in reconstruction. So a compression method having lower RMSE and corresponding high PSNR values could be recognized as a better scheme. Tables 1 – 2 show the performance results of the images decoded by the BTC, EDBTC, and the proposed method for both the grayscale and the colour images. From the test results, it is observed that the performance of the proposed method with the use of GA is better with lower RMSE and higher PSNR values than the BTC [3] and EDBTC [11] for all the grayscale and colour test images. It is illustrated by the clustered column chart shown in Fig 8 that compares the average values of RMSE and PSNR for all the test images, both the gray scale and colour images for the proposed and other methods. The average improvements in PSNR of the proposed method with the use of GA for the grayscale and colour test images over the EDBTC [11] are 6.66 and 6.51 dB, respectively. However the EDBTC [11] performs only slightly better than the BTC [3] with an average improvements in PSNR values of 0.48 and 0.49 dB, respectively, for the grayscale and colour test images. It indicates that the proposed method with GA performs much better than the BTC [3] and EDBTC [11] methods for both the grayscale and colour images. Figure 9 shows the reconstructed images by the proposed method and BTC [3] and EDBTC [11] methods for both the grayscale and colour images. It is to note that, though visually, all the test images have been reconstructed well, the proposed method with GA has shown comparatively much better performance with better PSNR values compared to the BTC [3] and EDBTC [11] methods for both the gray scale and colour images. The raggedness and the jagged appearance are greatly reduced from the reconstructed images by the proposed method with GA. Also, the ringing artifacts at sharp edges are considerably minimized in the reconstructed image. The proposed method based on the BTC has some invaluable characteristics, which make it in some practical applications a useful alternative to transform coding such as JPEG [6]. Transform coding has some real problems when storing high detail regions of an image at an acceptable bit rate. The BTC based proposed method has the ability to preserve edges and other such details of an image while maintaining an acceptable bit rate. It would be inappropriate to compare the proposed BTC based method with JPEG or any other
  11. 11. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 156 Table 1: MAE, RMSE, and PSNR values for BTC [3], EDBTC [11] and proposed methods for gray scale images Table 2: MAE, RMSE, and PSNR values for BTC [3], EDBTC [11] and proposed methods for colour images Images BTC EDBTC Proposed method MAE RMSE PSNR MAE RMSE PSNR MAE RMSE PSNR Cameraman 4.30 9.19 28.78 4.03 8.79 29.17 1.53 3.72 36.66 Barbara 5.58 8.96 28.70 4.79 8.14 29.60 1.14 3.16 37.83 Elaine 3.93 6.48 31.40 3.59 6.30 31.65 2.17 4.39 34.79 Lena 4.22 7.47 30.07 3.82 7.11 30.49 2.17 4.19 35.08 Lighthouse 5.72 9.36 28.54 8.62 5.14 29.24 3.04 1.05 38.29 Boat 5.19 8.68 28.94 4.76 5.23 29.40 1.99 4.24 34.16 House 3.17 6.12 31.87 2.86 5.76 32.39 2.19 4.25 35.03 Coin 2.09 5.31 33.58 1.92 5.08 33.97 0.87 2.23 41.11 Airfield 7.49 12.34 26.30 6.87 11.64 26.81 0.98 3.42 37.43 Crowd 6.06 10.10 28.04 5.58 9.67 28.43 1.30 3.47 37.31 AVERAGE 4.78 8.40 29.63 4.69 7.94 30.11 1.54 3.61 36.77 Images BTC EDBTC Proposed method MAE RMSE PSNR MAE RMSE PSNR MAE RMSE PSNR Eagle 1.36 4.96 34.18 1.31 4.65 34.74 0.79 2.22 40.38 Baboon 6.55 8.95 29.09 5.81 8.50 29.54 0.41 2.02 41.98 Barbara 5.52 8.73 29.31 4.96 8.19 29.86 0.56 2.36 40.65 Peppers 2.96 5.66 33.06 2.68 5.42 33.44 2.09 3.86 36.39 Lena 4.11 7.23 30.95 3.71 6.88 31.37 1.85 4.00 36.07 Cat 8.31 12.33 26.30 7.32 11.59 26.84 0.97 2.88 38.93 Football 3.33 5.41 33.46 2.96 5.18 33.84 2.09 4.26 35.53 Tulips 6.74 10.69 27.55 6.23 10.21 27.94 0.96 3.20 38.02 Tiffany 2.97 5.06 34.04 2.72 4.84 34.42 1.88 4.08 35.91 Zelda 3.84 6.14 32.35 3.40 5.79 32.87 1.82 3.84 36.43 AVERAGE 4.57 7.52 31.03 4.11 7.13 31.52 1.34 3.27 38.03
  12. 12. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 157 Fig. 8: Average values of RMSE and PSNR of proposed, BTC [3], and EDBTC [11] methods for all the gray scale and colour test images Original image BTC Original image BTC EDBTC Proposed method EDBTC Proposed method (a) Gray scale image - Cameraman (b) Colour image - Baboon Fig. 9: Reconstructed images by the proposed, BTC [3], and EDBTC [11] methods 8.40 7.94 3.61 29.63 30.11 36.8 7.52 7.13 3.27 31.03 31.52 38.03 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 1 2 3 4 5 6 RMSE,PSNR 1:BTC - gray 2:EDBTC - gray 3:Proposed method - gray 4:BTC - colour 5:EDBTC - colour 6:Proposed method - colour RMSE GRAY PSNR GRAY RMSE COLOUR PSNR COLOUR
  13. 13. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 158 transform based compression methods as the BTC can never compete with them because of they employ a number of different techniques such as discrete cosine transform (DCT)/wavelet transform (WT), run length encoding (RLE), and Huffman encoding together to perform the task. The proposed approach may be used to augment performance of existing algorithms. So one can incorporate the proposed BTC based method to a hybrid image compression method to achieve improved performance over transform based coding like JPEG. 5. CONCLUSIONS The proposed colour image data compression scheme using BTC and GA has shown to be superior to the conventional BTC and EDBTC methods. Several test images, both gray scale and colour images have been coded with the proposed method, and the resulting reconstructed images are much better in terms of visual quality. The blocking effect and false contour effect of the traditional BTC have been removed by using EDBTC and an optimal bit plane obtained with GA. EDBTC is similar to that of BTC with a few changes and it increases the performance when compared to BTC. Experimental results have shown improved and acceptable visual quality reconstructed image obtained using the proposed method with GA when compared to BTC [3] and EDBTC [11] for grayscale and colour images. Even though the performance of the proposed method is inferior to the transform coding methods, the proposed method has a lot of potential with its ability to store edge information. There are still other aspects, which make the proposed BTC based method a useful alternative for JPEG in some practical applications. The proposed method is quite simple and easy to implement, probably much simpler than the JPEG implementation. The encoding phase of the proposed method is fast and the decoding phase is even faster. Thus the proposed method may be incorporated in a hybrid image compression technique to achieve an optimal method for colour image compression. More research would be needed in this direction. REFERENCES [1] R.C. Gonzalez, R. Eugene; “Digital image processing”, Edition 3, 2008. [2] M. Doaa, A. Fatma, “Image Compression Using Block Truncation Coding”, Journal in selected areas of telecommunication, pp 9, 2011. [3] E. J. Delp and O. R. Mitchell, “Image compression using Block truncation coding”, IEEE Trans. Commun. Syst., vol. 27, no. 9, pp. 1335–1342, 1979. [4] W. Pennebaker and J. Mitchell, “JPEG still image data compression standards”, Van Nostrand Reinhold (1993). [5] K.WChan and K.L. Chan, “Optimization of multilevel block truncation coding”, Signal processing : Image communication, vol. 16, pp. 445, 2001. [6] P. Franti, O. Nevalinen and T. Kankoranta, “Compression of digital images by block truncation coding: A survey”, The computer journal, pp. 37, 1999. [7] H.B. Mitchell, N. Zilverberg and M. Avaraham, “A compression of different block truncation Coding algorithms for image compression”, Signal processing: Image communication, vol.6, pp. 77, 1994. [8] Y.C. Hu, “Improved moment preserving block truncation coding for image compression”, Electron. Lett., pp 39, 2003. [9] M. Kamel, C.T. Sun and G. Lian, “Image compression by variable block truncation coding with optimal threshold”, IEEE Trans on signal Processing vol.39, pp 208, 1991. [10] L.G. Chen and Y.C. Lu “A high quality MC-BTC codec for video signal processing”, IEEE Trans on circuits and systems for video technology CSVT-4, pp 92,1994.
  14. 14. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME 159 [11] J.M. Guo, and Y.F. Liu “High Capacity Data Hiding for Error-Diffused Block Truncation Coding”, IEEE Trans. on image processing, vol. 21, NO. 12, 2012. [12] C.C. Chang, Y.H. Chen, C.C. Lin “A data embedding scheme for color images based on genetic algorithm Absolute moment block truncation coding for color images”, Proc. Springer, Vol 13, pp 321-331, 2009 . [13] S.C. Tai, and W.J. Chen “Genetic algorithm approach to multilevel BTC”, ICICE Tran s.fundamentals, vol E82-A, no.8 Pp 1456-1457, 1999. [14] R.W. Floyd and L. Steinberg, “An adaptive algorithm for spatial gray scale”, Proc. SID Dig. Soc. Inform. Display, pp.36-37, 1975. [15] Ch. Ramesh, Dr. N.B. Venkateswarlu and Dr. J.V.R. Murthy, “A Novel K-Means Based JPEG Algorithm for Still Image Compression”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 339 - 354, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [16] 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. [17] S. S. Tamboli and Dr. V. R. Udupi, “Compression Methods using Wavelet Transform”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 314 - 321, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [18] K.P.Paradeshi, “Image Compression by EZW Combining Huffman and Arithmetic Encoder”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 3, 2013, pp. 297 - 307, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [19] 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.

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