A Compression Algorithm Based on Classified Interpolative Block Truncation Coding and Vector Quantization

Block truncation coding (BTC) [1] is an efficient image coding method that has been adopted to obtain the statistical properties of a block in image compression. Low computational complexity and superior channel error resisting ability make it attractive in real-time image compression. The BTC output data set includes a binary bit plane, which defines the quantization level of each pixel, and two reconstruction level values, determined by the mean and standard deviation of the block [1]. A modified BTC method [2, 3], denoted as AMBTC, achieves a smaller mean square error (MSE) and improves the quality of the reconstructed image. However, the threshold used to form the binary bit plane in these methods is set as the mean of the block, so they fail to achieve optimum quantization. In order to reduce the bit rate, vector quantization (VQ) [4] has been successfully applied to quantize the set of the two codewords generated by the BTC for a block [5]. In addition, Zeng proposed two interpolative BTC coding algorithms based on effective subsampling of the bit plane, which reduces the bit rate [6]. Furthermore, Zeng and Neuvo [7] combined the two methods mentioned above to develop a new method, denoted as VQIBTC in this paper, which largely increases the compression ratio. However, the use of VQ to quantize the two codewords greatly increases the complexity of the algorithm. In fact, VQIBTC, which quantizes each block using an equal number of quantization levels, is not efficient in improving the quality. In general, encoding a very smooth block using its mean