The authors consider 2-D predictive vector quantization (PVQ) of images subject to an entropy constraint and demonstrate the substantial performance improvements over existing unconstrained approaches. They describe a simple adaptive buffer-instrumented implementation of this 2-D entropy-coded PVQ scheme which can accommodate the associated variable-length entropy coding while completely eliminating buffer overflow/underflow problems at the expense of only a slight degradation in performance. This scheme, called 2-D PVQ/AECQ (adaptive entropy-coded quantization), is shown to result in excellent rate-distortion performance and impressive quality reconstructions of real-world images. Indeed, the real-world coding results shown demonstrate little distortion at rates as low as 0.5 b/pixel.
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机译：作者考虑了受熵约束的图像的二维预测矢量量化（PVQ），并证明了在现有无约束方法之上的显着性能改进。他们描述了这种二维熵编码的PVQ方案的简单自适应缓冲区插入的实现，该方案可以适应相关的可变长度熵编码，同时完全消除缓冲区的上溢/下溢问题，而仅以性能略有下降为代价。该方案称为2-D PVQ / AECQ（自适应熵编码量化），显示出出色的速率失真性能和令人印象深刻的真实图像质量重建。实际上，所示的实际编码结果表明，低至0.5 b /像素的速率几乎没有失真。
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