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A Lagrangian optimization approach to complexity-constrained TSVQ

We present a new variable rate tree-structured vector quantizer (TSVQ) design algorithm, in which the complexity-distortion tradeoff is explicitly managed using a Lagrangian optimization approach. The algorithm is greedy and uses subvector distortion measures to lower the encoding complexity. We show that we can obtain low complexity encoders for the Gauss-Markov source with similar distortion to that observed on standard variable rate TSVQ.