Title :
Multidimensional rotations for quantization
Author :
Hung, Andy C. ; Meng, Teresa H Y
Author_Institution :
Inf. Syst. Lab., Stanford Univ., CA, USA
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;
Conference_Titel :
Data Compression Conference, 1994. DCC '94. Proceedings
Conference_Location :
Snowbird, UT
Print_ISBN :
0-8186-5637-9
DOI :
10.1109/DCC.1994.305910
