TY - GEN
T1 - Frame permutation quantization
AU - Nguyen, Ha Q.
AU - Goyal, Vivek K.
AU - Varshney, Lav R.
N1 - Funding Information:
✩ This material is based upon work supported by the National Science Foundation under Grant No. 0729069. This work was also supported in part by a Vietnam Education Foundation Fellowship. ✩✩ This work was presented in part at the Inaugural Workshop of the Center for Information Theory and Its Applications at the University of California, San Diego, February 2006, and the Forty-fourth Annual Conference on Information Sciences and Systems, Princeton, NJ, March 2010. * Corresponding author. E-mail address: [email protected] (V.K. Goyal).
PY - 2010
Y1 - 2010
N2 - Frame permutation quantization (FPQ) is a new vector quantization technique using finite frames. In FPQ, a vector is encoded using a permutation source code to quantize its frame expansion. This means that the encoding is a partial ordering of the frame expansion coefficients. Compared to ordinary permutation source coding, FPQ produces a greater number of possible quantization rates and a higher maximum rate. Various representations for the partitions induced by FPQ are presented and reconstruction algorithms based on linear programming and quadratic programming are derived. Reconstruction using the canonical dual frame is also studied, and several results relate properties of the analysis frame to whether linear reconstruction techniques provide consistent reconstructions. Simulations for Gaussian sources show performance improvements over entropy-constrained scalar quantization for certain combinations of vector dimension and coding rate.
AB - Frame permutation quantization (FPQ) is a new vector quantization technique using finite frames. In FPQ, a vector is encoded using a permutation source code to quantize its frame expansion. This means that the encoding is a partial ordering of the frame expansion coefficients. Compared to ordinary permutation source coding, FPQ produces a greater number of possible quantization rates and a higher maximum rate. Various representations for the partitions induced by FPQ are presented and reconstruction algorithms based on linear programming and quadratic programming are derived. Reconstruction using the canonical dual frame is also studied, and several results relate properties of the analysis frame to whether linear reconstruction techniques provide consistent reconstructions. Simulations for Gaussian sources show performance improvements over entropy-constrained scalar quantization for certain combinations of vector dimension and coding rate.
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U2 - 10.1109/CISS.2010.5464814
DO - 10.1109/CISS.2010.5464814
M3 - Conference contribution
AN - SCOPUS:77953697266
SN - 9781424474172
T3 - 2010 44th Annual Conference on Information Sciences and Systems, CISS 2010
BT - 2010 44th Annual Conference on Information Sciences and Systems, CISS 2010
T2 - 44th Annual Conference on Information Sciences and Systems, CISS 2010
Y2 - 17 March 2010 through 19 March 2010
ER -