Title :
Principal component vector quantization for abrupt scene changes
Author :
Huang, S.C. ; Huang, Y.F.
Author_Institution :
Dept. of Electr. Eng., Notre Dame Univ., IN, USA
Abstract :
The authors present a vector quantization technique, referred to as principal component vector quantization (PCVQ), and investigate the problem of abrupt scene changes of video signals. This technique, featuring a simple design procedure, is implemented by an artificial neural network with a learning algorithm for online codebook design based on the local statistics for each difference scene. This network features online learning and a constant encoding time that is independent of the codebook size. The PCVQ was examined via simulation on an abrupt scene change problem which had nonstationary training sequences
Keywords :
image coding; learning (artificial intelligence); neural nets; vector quantisation; video signals; abrupt scene changes; artificial neural network; constant encoding time; learning algorithm; local statistics; nonstationary training sequences; online codebook design; principal component; simulation; vector quantization; video signals; Algorithm design and analysis; Artificial neural networks; Bandwidth; Data compression; Decoding; Encoding; Layout; Neural networks; Statistics; Vector quantization;
Conference_Titel :
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0593-0
DOI :
10.1109/ISCAS.1992.230504