This paper introduces an extension of conditional entropy-constrained RVQ (CEC-RVQ) that embodies trellis-coded quantization. The method, which we call conditional entropy-constrained trellis-coded residual vector quantization (CEC-TCRVQ), quantizes a supervector (made from a large number of neighboring vectors) to better extract the two-dimensional (2-D) correlation present in real images. Simulation results indicate that CEC-TCRVQ provides 0.3-0.4 dB improvement over CEC-RVQ for the 4/spl times/4 vector case and 1.3 dB improvement for the 8/spl times/8 case.
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机译：本文介绍了条件熵约束RVQ（CEC-RVQ）的扩展，它体现了网格编码量化。该方法称为条件熵约束网格编码残差矢量量化（CEC-TCRVQ），它对超矢量（由大量相邻矢量组成）进行量化，以更好地提取存在于其中的二维（2-D）相关性。真实的图像。仿真结果表明，对于4 / spl times / 4矢量情况，CEC-TCRVQ比CEC-RVQ改进了0.3-0.4 dB，对于8 / spl times / 8情况，改进了1.3 dB。
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