Title :
Variable rate self organizing neural networks for video compression
Author :
Thyagarajan, K.S. ; Erickson, Daniel
Author_Institution :
Dept. of Electr. & Comput. Eng., San Diego State Univ., CA, USA
fDate :
31 Oct-2 Nov 1994
Abstract :
This paper describes a design of Kohonen´s self-organizing neural networks as learning vector quantizers (LVQ) to compress video images. Both fixed rate and variable rate LVQs have been designed. For fixed rate LVQs, both full search and tree-structured codebooks are designed. Further, this paper describes the design of variable rate LVQs. Variable rate LVQs, structured as unbalanced trees, are found to provide improved performance of up to 3 dB peak SNR over comparable fixed rate LVQs
Keywords :
self-organising feature maps; vector quantisation; video coding; Kohonen´s self-organizing neural networks; SNR; design; fixed rate; full search codebooks; learning vector quantizers; performance; tree-structured codebooks; unbalanced trees; variable rate self organizing neural networks; video compression; video images; Artificial neural networks; Filtering; Image coding; Neural networks; Organizing; Pattern recognition; Pixel; Self-organizing networks; Signal to noise ratio; Video compression;
Conference_Titel :
Signals, Systems and Computers, 1994. 1994 Conference Record of the Twenty-Eighth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-6405-3
DOI :
10.1109/ACSSC.1994.471453
