International Journal of Computer Vision and Image Processing, 1(4), 1-18, October-December 2011 1 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Adaptive Lossy Image Compression (ALIC), Discrete Cosine Transform (DCT), Huffman Encoding, JPEG, Lossy Compression 1. INTRODUCTION As a result of bandwidth and storage limitations, image compression techniques are widely used in data transmission and data storage. The image compression is highly used in all applications like medical imaging, satellite imaging, etc. The image compression helps to reduce the size of the image, so that the compressed image could be sent over a space link or a computer network from one place to another in short amount of time. Also, the compressed image helps to store more number of images on the storage device (Gonzalez & Woods, 2005; Salomon, 2001; Khalifa, Harding, & Hashim, 2008; Singh, Sharma, & Sharma, 2009). The most popular image compression stan- dard is JPEG (Pennebaker & Mitchell, 1993; Lakhani, 2003); JPEG is described in Appendix B. However it is not an adaptive technique; it is independent on the image to be compressed. In fact, one significant complexity provided by the JPEG technique is the presence of the fixed Huffman tables in JPEG baseline which can be a bottleneck in hardware implementations. There are many adaptive compression algo- rithms presented in the literature. In Wu (2002) an adaptive sampling algorithm applied in the spectral domain, achieved by discrete cosine Image Compression Technique For Low Bit Rate Transmission Shaimaa A. El-said, Zagazig University, Egypt Khalid F. A. Hussein, Electronics Research Institute, Egypt Mohamed M. Fouad, Zagazig University, Egypt ABSTRACT A novel Adaptive Lossy Image Compression (ALIC) technique is proposed to achieve high compression ratio by reducing the number of source symbols through the application of an effcient technique. The proposed algorithm is based on processing the discrete cosine transform (DCT) of the image to extract the highest en- ergy coeffcients in addition to applying one of the novel quantization schemes proposed in the present work. This method is straightforward and simple. It does not need complicated calculation; therefore the hardware implementation is easy to attach. Experimental comparisons are carried out to compare the performance of the proposed technique with those of other standard techniques such as the JPEG. The experimental results show that the proposed compression technique achieves high compression ratio with higher peak signal to noise ratio than that of JPEG at low bit rate without the visual degradation that appears in case of JPEG. DOI: 10.4018/ijcvip.2011100101