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 DCT-based embedded image coding (EZDCT) (see Xiong et al., Z., 1996) and significance tree quantization (STQ) (see Davis, G. and Chawla, S., 1997). In the wavelet context, morphological representation of wavelet data has achieved the best compression performance. The representatives are morphological representation of wavelet data (MRWD) (see Servetto, S. et al., 1999) and significance-linked connected component analysis (see Chai, B.-B. et al., 1999). We show that, by proper reorganization of its coefficients, the block-based DCT can have similar characteristics, such as energy compaction, cross-subband similarity, decay of magnitude across subband, etc., to the wavelet transform. These characteristics can widen DCT applications relevant to image compression, image retrieval, 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 in peak signal-to-noise ratio by 1.0, 1.0, and 0.3 dB, respectively.