Abstract
We present a new algorithm for complexity-distortion optimization in tree-structured vector quantizers (TSVQ). The algorithm allows the user to specify the average rate and computational complexity budgets R and C, measured in bits and multiplications per sample, respectively. The output is an optimal—in a sense to be specified—TSVQ satisfying the constraints. The complexity budget is lower-bounded by the complexity of a binary TSVQ and upper-bounded by the complexity of a full-search entropy-constrained vector quantizer. Experimental results for synthetic and natural sources are given.
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