A novel wavelet based approach for near lossless image compression using modified duplicate free run length coding

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1 Journal of Theoretical and Applied Computer Science Vol. 8, No. 3, 2014, pp ISSN (printed), (online) A novel wavelet based approach for near lossless image compression using modified duplicate free run length coding Pacha Sreenivasulu 1, Kancharla Anitha Sheela 2 1 Department of Electronics and Communication Engineering, Parvathareddy Babulu Reddy Visvodaya Institute of Technology & Science, Kavali, India 2 Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, India vardhan.sreenivasulu@gmail.com, kanithasheela@gmail.com Abstract: In this paper we are presenting a three-stage near lossless image compression scheme. It belongs to the class of lossless coding which consists of wavelet based decomposition followed by modified duplicate free run-length coding. We go for the selection of optimum bit rate to guarantee minimum MSE (mean square error), high PSNR (peak signal to noise ratio) and also ensure that time required for computation is very less unlike other compression schemes. Hence we propose A wavelet based novel approach for near lossless image compression. Which is very much useful for real time applications and is also compared with EZW, SPIHT, SOFM and the proposed method is out performed. Keywords: image compression, wavelet, PSNR, MSE, EZW, SPIHT and SOFM, RLC 1. Introduction In general digital images contain lots of redundant information and hence they are usually compressed to remove redundancy, minimize the space required for storage or bandwidth for transmission particularly through wireless networks. If the redundancy removing process is reversible then the exact reconstruction of the original image can be achieved, it is called lossless image compression. In many scientific and medical applications and law enforcement (high performance applications) lossless image compression is the best choice. The techniques employed in lossless image compression are fundamentally rooted in entropy coding theory. The most widely used image compression techniques is the discrete wavelet transform. In JPEG 2000, discrete wavelet transform (DWT) is used as a core technology to compress still images as well as video. In DWT, signal energy concentrates to specific wavelet coefficients and it is a multi-resolution analysis which decomposes images into wavelet coefficients and scaling function. Advantages of wavelet transform include: 1) Able to extract and encode edge information, 2) Very good approximation with few coefficients, 3) Compare images of different resolutions, 4) Require linear time in the size of the image, 5) Wavelet decompositions are very fast, 6) Easy to compute. To sum up, at the present state of art technology only solution is to compress Multimedia data before storage and transmission and decompress it at the receiver for play back [1]. Discrete Cosine Transform (DCT) has been the Transform of choice in image compression

2 52 Pacha Sreenivasulu, Kancharla Anitha Sheela standard such as JPEG. DCT can be implemented in hardware. However, DCT suffer from blocking artifacts around sharp edges at low bit rate. In general; wavelets in recent years have gain widespread acceptance in signal processing and image compression in particular. Wavelet-based image coders are comprises three major components: A Wavelet filter bank that decomposes the image into wavelet coefficients which are then quantized in a quantizer and finally an entropy encoder encodes these quantized coefficients into an output bit stream (compressed image). Although the interplay among these components is important and one has the freedom to choose each of these components from a pool of candidates, it is often the choice of the wavelet filter that is crucial in determining the ultimate performance of the coder. A wide variety of wavelet-based image compression schemes have been developed in recent years [2-7,10]. Most of these well known Image coding algorithms use novel quantization and encoding techniques to improve Coding Performance (PSNR). However, they all use a fixed wavelet filter built into the algorithm for coding and decoding all types of color images, whether it is a natural, synthetic, medical, scanned or compound image. Wavelets have provided new class of powerful algorithm: They can be used for noise reduction, edge detection and compression. The usage of wavelets has superseded the use of DCTs for image compression in JPEG2000 image compression algorithm. Wavelet transform are of different types. The early work of Chen and Ramabadran [13] makes use of differential pulse code modulation (DPCM) [9,11] and a uniform scalar quantizer to ensure the maximum error of L. Ke and Marcellin [8] generalized it to any discrete error of. The authors used a DPCM coding scheme incorporating entropy-minimization of the quantized prediction errors. This paper is organized as follows: Importance and procedural steps of wavelet transforms is explained in section 2, proposed method for compression system is explained in 3, simulation results are presented in 4, conclusion and future scope is given in Wavelet transform of an image Wavelet transform is used to decompose an input signal into a series of successive lower resolution reference signals and their associated detail coefficients, which contains the information needed to reconstruct the reference signal at the next higher resolution level. In discrete wavelet transform, an image signal can be analyzed by passing it through analysis filter bank followed by decimation operation. This analysis filter bank which consists of both low pass and high pass filters at each decomposition stage is commonly used in image compression. When signal passes through these filters, it is split into two bands namely low frequency band and high frequency band. The low pass filter, which corresponds to averaging operation, extracts the coarse information of the signal. The high pass filter, which corresponds to differencing operation, extracts the detail information of the signal. The output of the filtering operation is then decimated by two. A two dimensional transform can be accomplished by performing two separate one dimensional transform (Fig.1) First, the image is filtered along the X-dimension using low pass and high pass analysis filter bank and decimated by two. Low pass filtered coefficients are stored on the left part of the matrix and high pass filtered on the right. Because of decimation, the total size of transformed image is same as the original image. It is then, followed by filtering the sub image along the Y-dimension and decimated by two. Finally, the image is split into four bands,, LH1 and HH1 through first level decomposition and second stage of filtering. Again the band is split into four bands viz LL2, HL2, LH2 and HH2 through second level decomposition [14].

3 A novel wavelet based approach for near lossless image compression 53 f (x,y) LL2 HL2 LH2HH2 LH1 HH1 LH1 HH1 Original 1 st Level 2 nd Level (a). Decomposition of the two dimensional DWT L H (b). Horizontal Transform-2 sub bands L H LH1 HH1 (c). Vertical Transform-2 sub bands ( 1 Level) LL2 HL2 LH2 HH2 LH1 HH1 LH1 HH1 3. Proposed method (d). 2 Level Figure 1. Wavelet decomposition In this proposed method of near lossless image compression, first the image is decomposed using a suitable wavelet wavelet, and only LL band is considered and the remaining components like i.e. LH, HL and HH bands are discarded because the beauty of the wavelet is to concentrate 98% of the visual energy only on the LL band and very small amount of visual energy and high frequency components (i.e. edges and boundaries) will be available in the remaining bands (LH, HL, HH). Hence in this method we are making use of this advantage. And then DPCM without quantizer is applied and modified duplicate free RLC applying on the output of DPCM. Algorithm explains the encoding and decoding process. Algorithm for proposed method: 1. Reading an image from the data base. 2. Decompose the image using selected suitable wavelet. 3. Discarding sub bands LH, HL, and HH. 4. Applying LL component to DPCM without quantizer.

4 54 Pacha Sreenivasulu, Kancharla Anitha Sheela 5. Separate the nonzero values from input vector a(i), and all nonzero values are placed in b(i). 6. Replace positions of nonzero values with 1 in a (i). 7. Now apply duplicate free length encoding on a(i)[12]. Figure 2. Proposed block diagram for encoder Figure 3. Proposed block diagram for decoder 4. Simulation results and discussions In this proposed method image is first decomposed using a suitable wavelet and only LL band is considered and the remaining bands i.e. LH, HL, HH bands are discarded. LL band of the decomposed image is passed through DPCM without quantizer and then modified duplicate free RLC is used for different types of images of size 256 x 256 pixels and algorithm is implemented using MATLAB. The PSNR (Peak Signal to Noise Ratio) based on MSE (Mean Square Error) is used as a measure of quality, MSE and PSNR are calculated by the following relations: MSE = 1 MXN M N ( x i, j yi, j ) i = 1 j = 1 2 (1) PSNR = 2 ( 255) 10 log (2) 10 MSE Here the quality of the reconstructed image can be improved further by considering remaining sub bands with suitable lossless coders or with hard or soft thresh holding on high frequency co-efficients. But either of the above process compression ratio will reduce.

5 A novel wavelet based approach for near lossless image compression 55 (a) (b) (c) (d) (e) (f) Figure 4. Subfigures a to e shows the original images of Cameraman, Coins, Bag, Rice and Lifting Body, subfigure f shows the reconstructed image of Lifting Body Table 1: Performance evaluation of proposed method for different wavelets for different images Parameter Compression Ratio MSE PSNR Image WAVELETS HAAR BIOR 6.8 DAUBECHIES COIFLETS Cameraman Coins Bag Rice Lifting body Cameraman Coins Bag Rice Lifting body Cameraman Coins Bag Rice Lifting body

6 56 Pacha Sreenivasulu, Kancharla Anitha Sheela (a) (b) (c) (d) (e) Figure 5. Comparison of compression ratios for different wavelets for different images Table 2. Performance comparison of different methods, for Lifting body image Technique MSE PSNR CR Proposed method SPIHT EZW SOFM Conclusion and future scope A wavelet based novel approach for near lossless image compression using duplicate free RLC is proposed in which a suitable wavelet is used to decompose the image under compression and here only LL band is considered and all the remaining sub-bands are dis-

7 A novel wavelet based approach for near lossless image compression 57 carded. By making use of the DPCM without quantizer and modified duplicate free RLC, the proposed method gains maximum compression ratio with very good visual quality in both qualitative and quantitative methodology. Experimental results verified that the proposed method out performs. As a future work we want to achieve better compression ratio and PSNR by adapting advanced entropy base coding techniques. References [1] Villasenor, J., Belzer, B., Liao, J.: Wavelet Filter Evaluation for Image Compression. IEEE Transactions on Image Processing, Vol. 2, pp , August [2] Arora, R. et al: An Algorithm for Image Compression Using 2D Wavelet Transform. International Journal of Engineering Science and Technology (IJEST), Vol. 3, No. 4, Apr [3] Anitha Sheela, K., Sreenivasulu, P., Asha Rani, M.: Neural Networks and Lifting Scheme based Image Compression. World Academy of Science, Engineering and Technology 69, [4] Raja, S. P., Suruliandi, A.: Analysis Of Efficient Wavelet based Image Compression Techniques Second International conference on Computing, Communication and Networking Technologies. [5] Xiao, W., Liu, H.: Using Wavelet Networks in Image Compression Seventh International Conference on Natural Computation. [6] Walker, J. S.: A Primer on Wavelets and Their Scientific Applications, Second edition, Taylor & Francis Group, LLC, Beijing, Jun [7] Peng, X., Xu, J., Wu, F.: Directional Filtering Transform for Image/Intra-Frame Compression. IEEE Transactions On Image Processing, Vol. 19, No. 11, November [8] Ke, L., Marcellin, M.: Near-lossles image compression: Minimum entropy, constrained-error DPCM. IEEE Trans. Image Process., vol.7, no. 2, pp , Feb [9] Reichel, J., Menegaz, G., Nadenau, M. J., Kunt, M.: Integer wavelet transform for embedded lossy to lossless image compression. IEEE Trans. Image Process., vol. 10, no. 3, pp , Mar [10] Adams, M. D., Kossentini, F.: Reversible integer-to-integer wavelet transforms for image compression: Performance evaluation and analysis. IEEE Trans. Image Process., vol. 9, no. 6, pp , Jun [11] Yea, S., Pearlman, W. A.: A wavelet-based two-stage near-lossless coder. IEEE transactions on image processing, vol. 15, no. 11, November [12] Al-Wahaib, M. S., Wong K.-S.: A Lossless Image Compression Algorithm Using Duplication Free Run-Length Coding Second International Conference on Network Applications, Protocols and Services. [13] Chen, K., Ramabadran, T.: Near-lossless compression of medical images through entropycoded DPCM. IEEE Trans. Med. Imag., vol. 13, no. 9, pp , Sep [14] Marpe, D., Blattermann, G., Ricke, J., Maab, P.: A two-layered wavelet-based algorithm for efficient lossless and lossy image compression. IEEE Trans. Circuits Syst. Video Technol., vol. 10, no. 10, pp , Oct