Title :
A Lagrangian optimization approach to complexity-constrained TSVQ
Author_Institution :
Comput. Sci. Dept., Univ. Libre de Bruxelles, Belgium
Abstract :
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.
Keywords :
Gaussian processes; Markov processes; computational complexity; optimisation; rate distortion theory; source coding; tree data structures; variable rate codes; vector quantisation; Gauss-Markov source; Lagrangian optimization approach; TSVQ; complexity-distortion tradeoff; encoding complexity; greedy algorithm; low complexity encoders; subvector distortion measures; variable rate tree-structured vector quantizer; Algorithm design and analysis; Binary trees; Constraint optimization; Distortion measurement; Encoding; Lagrangian functions; Nonlinear distortion; Optimization methods; Partitioning algorithms; Vector quantization;
Journal_Title :
Signal Processing Letters, IEEE
