Image compression is the application of data compression on digital images. In effect, the objective is to reduce redundancy of image data in order to be able to store or transmit data in an efficient form. There have been many types of compression algorithms developed. This work exploits an image compression based on Set Partitioning In hierarchical Tree (SPIHT) methods.In this work, the RGB image was transformed into YCbCr image, the goal of this transforming is to prepare the image for encoding process by eliminating any irrelevant information, the Cb and Cr bands are down sampled due to their poor spatial resolution. Then the compression technique is started by applying the wavelet transform on each component,the first sub band (i.e. LL sub band) is coded by using Discrete Cosine Transform (DCT), and uniform quantization. The other sub bands are coded by hierarchical uniform quantization, and then the SPIHT method was applied on each color band separately. At the end, some spatial coding steps were applied on List of Significant Pixels (LSP) like (Run Length Encoding (RLE) and Shift Coding) to gain more compression.Six color images are used to test the system performance, At first, a comparison between Haar and Tap9/7 wavelet transforms were made, the results show that tap9/7 give better PSNR on the selected images, so this work continued by using Tap9/7. Then many tests were made to select the best parameter values for the uniform quantization. At last a comparison between the test results of the original SPIHT, Modified SPIHT and Modified SPIHT with DCT & (Run Length Encoding) RLE were made and the result shows that the last method gives good PSNR with relatively high CR).