A hybrid gray image representation using spatial- and DCT-based approach with application to moment computation

In this paper, a novel hybrid gray image representation using spatial- and DCT-based approach is presented. In the first phase, according to the bintree decomposition principle under the specified error, an S-tree spatial data structure (SDS) is used to represent the decomposed bintree of the input gray image. In the constructed S-tree SDS, the leaves are partitioned into two types, namely the homogeneous leaves and the nonhomogeneous leaves. The homogeneous leaf is used to represent one rectangular or square homogeneous subimage with smooth, i.e., low frequency, content and the nonhomogeneous leaf is used to represent one nonhomogeneous subimage with nonsmooth, i.e., high frequency, content. In the second phase, each nonhomogeneous leaf is encoded by the DCT-based coding scheme for reducing the memory requirement. Based on some real gray images, experimental results show that our proposed gray image representation over the previously published S-tree- and shading-based SDS has about 63.08% memory-saving improvement ratio in average. Finally, we investigate the computational benefit when computing moments on our proposed gray image representation directly.