Nearly optimal vector quantization via linear programming
Jyh-Han Lin, Jeffrey Scott Vitter · 2003
The authors present new vector quantization algorithms. The new approach is to formulate a vector quantization problem as a 0-1 integer linear program. They first solve its relaxed linear program by linear programming techniques. Then they transform the linear program solution into a provably good solution for the vector quantization problem. These methods lead to the first known polynomial-time full-search vector quantization codebook design algorithm and tree pruning algorithm with provable worst-case performance guarantees. They also introduce the notion of pseudorandom pruned tree-structured vector quantizers. Initial experimental results on image compression are very encouraging.>