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Amplitude quantization and permutation encoding are two of the many approaches to efficient digitization of analog data. It is shown in this paper that these seemingly different approaches actually are equivalent in the sense that their optimum rate versus distortion performances are identical. Although this equivalence becomes exact only when the quantizer output is perfectly entropy coded and the permutation code block length is infinite, it nonetheless has practical consequences both for quantization and for permutation encoding. In particular, this equivalence permits us to deduce that permutation codes provide a readily implementable block-coding alternative to buffer-instrumented variable-length codes. Moreover, the abundance of methods in the literature for optimizing quantizers with respect to various criteria can be translated directly into algorithms for generating source permutation codes that are optimum for the same purposes. The optimum performance attainable with quantizers (hence, permutation codes) of a fixed entropy rate is explored too. The investigation reveals that quantizers with uniformly spaced thresholds are quasi-optimum with considerable generality, and are truly optimum in the mean-squared sense for data having either an exponential or a Laplacian distribution. An attempt is made to provide some analytical insight into why simple uniform quantization is so good so generally.