Multidimensional rotations for quantization

Author

Hung, Andy C. ; Meng, Teresa H Y

Author_Institution

Inf. Syst. Lab., Stanford Univ., CA, USA

fYear

1994

fDate

29-31 Mar 1994

Firstpage

32

Lastpage

41

Abstract

Laplacian and generalized Gaussian data arise in image and speech coding. Simple rotations of independent, identically distributed Laplacian and generalized Gaussian data in multiple dimensions can improve the granular and overload characteristics for quantization. In this paper, we describe the practical properties of multidimensional rotations for both scalar and lattice quantization and then apply them to image compression over noisy channels

Keywords

analogue-digital conversion; data compression; encoding; image coding; random noise; speech coding; telecommunication channels; Laplacian data; Walsh-Hadamard transform; generalized Gaussian data; granular characteristics; image coding; image compression; independent identically distributed data; lattice quantization; multidimensional rotations; noisy channels; overload characteristics; scalar quantization; speech coding; vector quantisation; Filters; Gaussian distribution; Image coding; Laplace equations; Lattices; Multidimensional systems; Quantization; Random variables; Reflection; Speech coding;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Compression Conference, 1994. DCC '94. Proceedings

Conference_Location

Snowbird, UT

Print_ISBN

0-8186-5637-9

Type

conf

DOI

10.1109/DCC.1994.305910

Filename

305910