Optimal Index Assignment for Multiple Description Scalar Quantization

Zhang, G; Klejsa, J; Kleijn, WB

Optimal Index Assignment for Multiple Description Scalar Quantization

Klejsa, J Kleijn, WB

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Field	Value	Language
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-4521-542X	en_US
dc.contributor.author	Klejsa, J	en_US
dc.contributor.author	Kleijn, WB	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117737
dc.description.abstract	We provide a method for designing an optimal index assignment for scalar K-description coding. The method stems from a construction of translated scalar lattices, which provides a performance advantage by exploiting a so-called staggered gain. Interestingly, generation of the optimal index assignment is based on a lattice in K-1 dimensional space. The use of the K-1 dimensional lattice facilitates analytic insight into the performance and eliminates the need for a greedy optimization of the index assignment. It is shown that that the optimal index assignment is not unique. This is illustrated for the two-description case, where a periodic index assignment is selected from possible optimal assignments and described in detail. The new index assignment is applied to design of a K-description quantizer, which is found to outperform a reference K-description quantizer at high rates. The performance advantage due to the staggered gain increases with increasing redundancy among the descriptions.	en_US
dc.title	Optimal Index Assignment for Multiple Description Scalar Quantization	en_US
utslib.for	010505 Mathematical Aspects of Quantum and Conformal Field Theory, Quantum Gravity and String Theory	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access

Abstract:

We provide a method for designing an optimal index assignment for scalar K-description coding. The method stems from a construction of translated scalar lattices, which provides a performance advantage by exploiting a so-called staggered gain. Interestingly, generation of the optimal index assignment is based on a lattice in K-1 dimensional space. The use of the K-1 dimensional lattice facilitates analytic insight into the performance and eliminates the need for a greedy optimization of the index assignment. It is shown that that the optimal index assignment is not unique. This is illustrated for the two-description case, where a periodic index assignment is selected from possible optimal assignments and described in detail. The new index assignment is applied to design of a K-description quantizer, which is found to outperform a reference K-description quantizer at high rates. The performance advantage due to the staggered gain increases with increasing redundancy among the descriptions.

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http://hdl.handle.net/10453/117737