本文讨论了最佳熵和均方差约束无记忆型量化器静止图象子带编码系统中性能效率的理论和实际估算。利用一失真理论概念推导出了一种特殊形式的函数，从而给出了用信噪比和冗余减少量表达的性能改善。

This paper deals with the estimation of the theoretical and practical performance efficiency of a subband coding system for still images using memoryless quantizers optimized to entropy and mean-square error constraints. An approach with the concept of rate-distortion theory has allowed to derive a function in special form,which gives the performance gain in terms of signal-to-noise ratio and redundancy reduction. The theoretical upper bounds on entropy coded bit rate reduction were found to be 1.10; 1.51 and 2.10 bits per pixel for M = 4,7 and 16 subbands, respectively. Some simulation results concerning practical performance are presented and interpreted on test images.