An entropy-constrained residual vector quantization design algorithm is used to design codebooks for image coding. Entropy-constrained residual vector quantization has several important advantages. It can outperform entropy-constrained vector quantization in terms of rate-distortion performance, memory, and computation requirements. It can also be used to design vector quantizers with relatively large vector sizes and high output rates. Experimental results indicate that good image reproduction quality can be achieved at relatively low bit rates. For example, a peak signal-to-noise ratio of 30.09 dB is obtained for the 512/spl times/512 LENA image at a bit rate of 0.145 b/p.
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机译：熵约束的残差矢量量化设计算法用于设计用于图像编码的码本。熵约束残差矢量量化具有几个重要优点。就速率失真性能，内存和计算要求而言，它可以胜过熵约束的矢量量化。它也可以用于设计具有相对较大的矢量大小和高输出速率的矢量量化器。实验结果表明，可以在较低的比特率下获得良好的图像再现质量。例如，对于512 / spl次/ 512 LENA图像，以0.145 b / p的比特率获得了30.09 dB的峰值信噪比。
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