High-speed cluster scanning scheme for three-dimensional constellated image processing

Due to the Human Visual System (HVS) the response to well brightened images is frequency dependent. The Discrete Cosine Transform (DCT) - based compression schemes are capable of separating the perceptually significant information in an image from the information that the eye cannot perceive. Hence in image compression, DCT is most widely used transform. In this paper, an algorithm for compressing images based on Cluster 3D-DCT technique is proposed. This method initially segments the two-dimensional image and then forms the clusters within each segment. These segments are then parallely processed to form a three-dimensional cube of 8*8*8 pixels and is processed with DCT. Thereafter the quantization and zigzag scanning processes are implemented. After completing the processes, the 1D data vector formed facilitates in achieving better compression using run-length coding. The performance of the algorithm is verified by plotting graphs, and determining its dominance over other primitive scanning techniques. The processing time and time required to segment the image along with cluster assignment time is tabulated. The cluster scanning which is a parallel progression proves to be faster compared to Spiral, ZigZag and Interleaving scanning schemes.