Abstract—In this paper we present simulation results for the application of a bandwidth efficient algorithm (mapping algorithm) to an image transmission system. This system considers three different real valued transforms to generate energy compact coefficients. First results are presented for gray scale and color image transmission in the absence of noise. It is seen that the system performs its best when discrete cosine transform is used. Also the performance of the system is dominated more by the size of the transform block rather than the number of coefficients transmitted or the number of bits used to represent each coefficient. Similar results are obtained in the presence of additive white Gaussian noise. The varying values of the bit error rate have very little or no impact on the performance of the algorithm. Optimum results are obtained for the system considering 8x8 transform block and by transmitting 15 coefficients from each block using 8 bits. Keywords—Additive white Gaussian noise channel, mapping algorithm, peak signal to noise ratio, transform encoding. I. INTRODUCTION IGITAL image processing is a rapidly evolving field with extensive applications in the field of mobile technology. In an increasing number of applications [1] video and images are transmitted and received over portable wireless devices such as cellular phones, laptop computers and cameras used in surveillance. Although image transmission is highly desirable in many applications, a limiting factor has been the use of bandwidth and energy efficient methods for transmission. Also in most of the cases a reduction in signal bandwidth is generally accompanied with a decrease in the delivered image quality. Several image data compression techniques are discussed in [1]. Although the optimum image compression method largely depends on the type of image, recently however considerable attention has been given to the technique of transform encoding [2]. Transform coding methods provide high energy compaction within a small number of decorrelated coefficients thus Manuscript received June 30, 2008. Shivali D. Kulkarni is with the Department of Electronics and Telecommunication, K. J. Somaiya College of Engineering, Mumbai 400 077, India (e-mail: kulkarni.shivali.d@gmail.com). Ameya K. Naik is with the Department of Electronics and Telecommunication, K. J. Somaiya College of Engineering, Mumbai 400 077, India (e-mail: ameyaknaik@yahoo.com). Nitin S. Nagori is with the Department of Electronics and Telecommunication, K. J. Somaiya College of Engineering, Mumbai 400 077, India (e-mail: nitinnagori@yahoo.com). eliminating redundancy. While the bit rate reduction is achieved, a strong data dependency is created between the pixels. This increases image sensitivity to channel noise and subsequently, affects the image quality considerably. A number of channel coding schemes have been proposed for reducing the channel noise. Channel coding schemes tend to introduce redundancy resulting in bandwidth expansion. Hence a trade off has to be achieved between the data compression obtained by source coding and data expansion due to channel coding. A better scheme would be to compress the source information as completely as possible and then to allow for the inherent redundancy due to channel coding. Although image transformation methods provide energy compaction in only a few coefficients, a critical issue is the transmission of high magnitude transform coefficients. In this paper we present the mapping algorithm for transmitting transform coefficients using least number of bits. The performance of this algorithm is observed for different real valued transformations such as discrete cosine transform (DCT)[3-5], discrete sine transform (DST)[5-7] and discrete Hartley transform (DHT)[6,8]. It is seen that the discrete cosine transform gives a better energy compaction as compared to the other transforms. Furthermore the performance of these algorithms is evaluated for different sizes of transform block, number of coefficients transmitted from each block and number of bits used to represent each pixel coefficient. It is noted that the size of the transform block has more prominent effect on the performance of the transmission system as compared to the other parameters. The remainder of the paper is organized as follows. Section II describes the process of image transmission using a channel encoding scheme. Section III discusses the mapping algorithm used for compression along with its performance measures. This is followed by a brief description of the various real valued image transforms used by the system (section IV). The simulation results are presented in section V and finally the concluding remarks are given in section VI. II. SYSTEM MODEL The communication system shown in fig.1 is simulated using Matlab v. 7.6. The model (fig. 1) consists of a source encoder which accepts a still image as the input. The image undergoes a real valued image transformation such as DCT, DST and DHT. The coefficients obtained are then scanned [1] using zig-zag technique and only a few coefficients are considered from each scanning block for further processing. A Comparison of Real Valued Transforms for Image Compression Shivali D. Kulkarni, Ameya K. Naik, and Nitin S. Nagori D World Academy of Science, Engineering and Technology International Journal of Electrical and Computer Engineering Vol:2, No:7, 2008 1398 International Scholarly and Scientific Research & Innovation 2(7) 2008 scholar.waset.org/1307-6892/9489 International Science Index, Electrical and Computer Engineering Vol:2, No:7, 2008 waset.org/Publication/9489