The method of block truncation coding (BTC) was originally proposed by Delp and Mitchell and later extended to color images by others. The idea is to retain important visual features while discarding details which are not to be noticeable to human observers. In our paper, we further explore the block truncation coding method by minimizing the within group variance measure proposed by Otsu and the information distance suggested by Kullback to divide every 4 by 4 subimage into two classes, and an intuitive vector quantizer to further compress the coded output. As a result of the combined application of BTC and vector quantization methods, we get better bit rates (bits per pixel) for the test image used in experiments without significant perceivable errors in its appearance. First, we divide a color image into 4 by 4 small nonoverlapping blocks. The Otsu or Kullback thresholding technique is then used as an optimal method to minimize the mean square error of classifying each pixel in a block into two classes and encode it in one-bit adaptive vector quantizer. After classification, for each 4 by 4 block, there is a bitmap corresponding to one-bit adaptive vector quantizer and a six-dimensional mean vector corresponding to each of the two classes. In the second part, the vector quantizer proposed by Linde, Buzo and Gary (known as LBG) is used to compress the bit-map and mean vectors separately. This is a six- dimensional signal compression for the mean vectors and a binary compression for the bitmap. Vector quantization of these BTC output results in a reduction of the bit rate of the coder. By using BTC and vector quantization methods, we have obtained 1.0 bit/pixel compression result for a color image of size 512 by 480 given with 8 bits/pixel and R, G, B specifications. The mean square error was also measured as low as 0.07 without much deformation in the reconstructed image.