International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 5 Issue: 8 240 – 243 _______________________________________________________________________________________________ 240 IJRITCC | August 2017, Available @ http://www.ijritcc.org _______________________________________________________________________________________ An Energy Efficient and High Speed Image Compression System Using Stationary Wavelet Transform V Srinivasa Rao Dept of ECE SVECW Bhimavaram, AP, India Rajesh K Panakala Dept of ECE PVPSIT Vijayawada, AP, India P.Rajesh Kumar Dept of ECE AUCE Vizag, AP, India Abstract— Image compression is one of the interesting domains nowadays in all areas of research. Everybody working with huge of amount of data in their daily life. In-order to deal with such huge amount of data, there is a need to store and compress the data. So there is a need to develop a system to compress and store the data. JPEG 2000 is a system to achieve this object. In this paper an area efficient and high speed JPEG2000 architecture has been developed to compress the image data. To implement JPEG2000 system, here a transform called stationary wavelet transform has been used. Stationary wavelet transform reduces the bottlenecks existing in the wavelet transform. Stationary wavelet transform avoids the problem of invariance-translation of the already existing discrete wavelet transform. The proposed stationary wavelet transform based JPEG2000 improves the speed and efficiency of power compared to the discrete wavelet transform based JPEG2000. Many image compression applications such as tele-medicine, satellite imaging, medical imaging require high-speed, low power compression techniques with small chip area. This paper has an analysis on the speed of JPEG2000 using stationary wavelet transform and it will be compared theoretically and practically with the JPEG2000 using discrete wavelet transform. The amount of information missing in the test image usually been very small when compared to the DWT based JPEG2000.The MSE and PSNR values proved to be better when compared to the DWT based JPEG2000. The proposed SWT based JPEG2000 compresses and decompresses the image at a faster rate than the DWT based JPEG2000.Finally the design will be implemented in XILINX Virtex-4 FPGA Kit. .The power consumption of the proposed method proved to be 290mW compared to other types of compression techniques. Keywords—Compression, Stationary wavelet transform, Discrete wavelet transform JPEG2000, MSE,PSNR. __________________________________________________*****_________________________________________________ I. INTRODUCTION JPEG2000 is an efficient image compression technique when compared to the JPEG. Image compression plays a vital role in many applications such as tele medicine, satellite images, internet etc. Already existing architectures of JPEG2000 consumes more power and compresses the image at a slower rate. Hence there is a need to develop an architecture which consumes less energy and operates at higher speed. The conventional DWT used in image compression is not shift variant. This means that the DWT translated version of a signal is not same as the original signal. Hence there is a need to use stationary wavelet transform based JPEG2000 image compression system. In stationary wavelet transform(SWT) the high pass filters and low pass filters are applied to the data in the block segments of an image. In SWT modify the filters by padding zeroes. Stationary wavelet transform based JPEG 2000 is slightly computational intensive than discrete wavelet transform based JPEG2000[1][2]. In multi-scale signal processing, wavelet is a time-frequency analysis that has been widely used in the field of image processing such as denoising, compression, and segmentation. For each modification in the circuit the delay and power will be reduced[3][5]. The SWT algorithm isvery simple and close to DWT. To calculate the decimated DWT for a given signal of length by computing approximation and detail coefficients for every possible sequence. The simulation results show the reduction in power and delay. The stationary wavelet decomposition is more tractable than the wavelets.SWT has the advantage of maintaing the same number of coefficients throughout all scales. SWT having 2nk coefficients where n is the length of the signal and k is the number of scales is having high redundancy which is particularly suitable for image compression applications. The paper is organized as follows: Section I deals with introduction, section II deals with related work, section III covers proposed work, section IV covers results and section V states the conclusion of the work. II. REVIEW OF PREVIOUS WORK A) Stationary Wavelet Transform (SWT) Among the different tools of multi-scale signal processing, wavelet is a time-frequency analysis that has been widely used in the field of image processing such as denoising, compression, and segmentation. Wavelet-based denoising provides multi-scale treatment of noise, down-sampling of sub-band images during decomposition and the thresholding of wavelet coefficients may cause edge distortion and artifacts in the reconstructed images. To improve the limitation of the traditional wavelet transform, a multi-layer stationary wavelet transform (SWT) was adopted in this paper, as illustrated in Figure 1. In Figure 1, Hj and Lj represent high-pass and low-pass filters at scale j, resulting from the interleaved zero padding of filters Hj-1 and Lj-1 (j>1). LL0 is the original image and