Improved BTC Algorithm for Gray Scale Images Using K-Means Quad Clustering

With images replacing textual and audio in most technologies, the volume of image data used in everyday life is very large. It is thus important to make the image file sizes smaller, both for storage and file transfer. Block Truncation Coding (BTC) is a lossy moment preserving quantization method for compressing digital gray level images. Even though this method retains the visual quality of the reconstructed image it shows some artifacts like staircase effect, etc. near the edges. A set of advanced BTC variants reported in literature were analyzed and it was found that though the compression efficiency is increased, the quality of the image has to be improved. An Improved Block Truncation Coding using k-means Quad Clustering (IBTC-KQ) is proposed in this paper to overcome the above mentioned drawbacks. A new approach of BTC to preserve the first order moments of homogeneous pixels in a block is presented. Each block of the input image is segmented into quad-clusters using k-means clustering algorithm so that homogeneous pixels are grouped into the same cluster. The block is then encoded by means of the pixel values in each cluster. Experimental analysis shows an improvement in the visual quality of the reconstructed image with high Peak Signal-to-Noise Ratio (PSNR) values compared to the conventional BTC and other modified BTC methods.