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Block Truncation Coding (BTC) techniques, to date, utilize a two-level image block code. This thesis presents and studies a new BTC method employing a three-level image coding technique. This new method is applied to actual image frames and compared with other well-known block truncation coding techniques. The method separates an image into disjoint, rectangular regions of fixed size and finds the highest and lowest pixel values of each. Using these values, the image block pixel value range is calculated and divided into three equal sections. The individual image block pixels are then quantized according to the region into which their pixel value falls; they are quantized to a 2 if they fall in the upper third , a 1 in the middle third, and a O if in the lower third range region. Thus, each pixel now requires only two bits for transmission. This is one bit per pixel more than the other well-known BTC techniques and thus it has a smaller compression ratio. When the BTC techniques were applied to actual images, the resulting 3LBTC reconstructed images had the smallest mean-squared-error of the techniques applied. It also produced favorable results in terms of the entropy of the reconstructions as compared to the entropy of the original images. The reconstructed images were also very good replicas of the originals and the 3LBTC process had the fastest processing speed. For applications where coding and reconstruction speed are crucial and bandwidth is not critical, the 3LBTC provides an image coding solution.