Title :
Image coding using feature map finite-state vector quantization
Author :
Xu, Meina ; Kuh, Anthony
Author_Institution :
Dept. of Electr. Eng., Hawaii Univ., Honolulu, HI, USA
fDate :
7/1/1996 12:00:00 AM
Abstract :
A vector quantization (VQ) scheme with finite memory called feature map finite-state vector quantization (FMFSVQ) is presented. The FMFSVQ takes advantage of good topological ordering so that the design of state codebooks is simplified. Our FMFSVQ also has no duplication of state codebooks, no synchronization required between encoder and decoder, and a very simple decoder. An adaptive FMFSVQ scheme is also proposed. Experimental results are presented for different super codebook sizes and different state codebook sizes.
Keywords :
image coding; self-organising feature maps; unsupervised learning; vector quantisation; FMFSVQ; decoder; encoder; experimental results; feature map finite-state vector quantization; image coding; state codebook sizes; state codebooks design; supercodebook sizes; topological ordering; unsupervised learning; Bit rate; Computational complexity; Decoding; Feature extraction; Histograms; Image coding; Indexing; Transmitters; Unsupervised learning; Vector quantization;
Journal_Title :
Signal Processing Letters, IEEE
