title: Conditional entropy-constrained residual VQ with application to image coding.
abstract: This paper introduces an extension of entropy constrained residual vector quantization (VQ) where intervector dependencies are exploited. The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors. When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements, moreover, it can be designed to support progressive transmission in a natural way. It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature. This is due partly to the joint optimization between the residual vector quantizer and a high order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction.
authors.affiliation: Digital Signal Processing Lab., Georgia Inst. of Technol., Atlanta, GA.
authors.forename: F
authors.initials: F
authors.lastname: Kossentini
authors.forename: W C
authors.initials: WC
authors.lastname: Chung
authors.forename: M T
authors.initials: MT
authors.lastname: Smith