Complexity-constrained tree-structured vector quantizers (Q1603617)

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.