Title :
Predictive vector quantizer using constrained optimization
Author :
Rizvi, Syed A. ; Nasrabadi, Nasser M.
Author_Institution :
Dept. of Electr. & Comput. Eng., State Univ. of New York, Buffalo, NY, USA
Abstract :
A joint optimization technique is developed for designing the predictor and quantizer of a predictive vector quantizer (PVQ). The proposed technique is based on a constrained optimization technique that makes use of a Lagrangian formulation and iteratively solves the Lagrangian error function to obtain a locally optimal solution for the predictor and quantizer. Simulation results show that the proposed PVQ design outperforms the conventional PVQ schemes, such as the closed-loop design and the jointly-optimized technique.<>
Keywords :
codecs; filtering and prediction theory; image coding; iterative methods; optimisation; vector quantisation; Lagrangian error function; Lagrangian formulation; PVQ; constrained optimization technique; joint optimization technique; locally optimal solution; predictive vector quantizer; predictor design; quantizer design; Bit rate; Constraint optimization; Data compression; Design optimization; Image coding; Image reconstruction; Iterative methods; Lagrangian functions; Student members; Vector quantization;
Journal_Title :
Signal Processing Letters, IEEE
