Morphological representation of DCT coefficients for image compression

Recent success in discrete cosine transform (DCT) image coding is mainly attributed to recognition of the importance of data organization and representation. Currently, there are several competitive DCT-based coders such as Xiong et al. DCT-based embedded image coding (EZDCT) and Davis and Chawla's significance tree quantization (STQ). In the wavelet context, morphological representation of wavelet data has achieved the best compression performance. The representatives are Servetto et al. morphological representation of wavelet data (MRWD) and Chai et al. significance-linked connected component analysis. In this paper, we show that the block-based DCT by proper reorganization of its coefficients can have similar characteristics to wavelet transform, such as energy compaction, cross-subband similarity, decay of magnitude across subband, etc. These characteristics can widen DCT applications relevant to image compression, image retrieving, pattern recognition, etc. We then present an image coder utilizing these characteristics by morphological representation of DCT coefficients (MRDCT). The experiments show that MRDCT is among the state-of-the-art DCT-based image coders reported in the literature. For example, for the Lena image at 0.25 bpp, MRDCT outperforms JPEG, STQ, and EZDCT by 1.0, 1.0, and 0.3 dB in peak signal-to-noise ratio, respectively.