𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Building a hierarchy with neural networks: an example—image vector quantization

✍ Scribed by Jackel, L. D. ;Howard, R. E. ;Denker, J. S. ;Hubbard, W. ;Solla, S. A.


Book ID
115335383
Publisher
The Optical Society
Year
1987
Tongue
English
Weight
881 KB
Volume
26
Category
Article
ISSN
1559-128X

No coin nor oath required. For personal study only.


📜 SIMILAR VOLUMES


Fast image vector quantization using a m
✍ Robert Li; Earnest Sherrod; Jung Kim; Gao Pan 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 259 KB 👁 2 views

The basic goal of image compression through vector generates the address of the codevector specified by Q(x); and quantization (VQ) is to reduce the bit rate for transmission or data a decoder, which uses this address to generate the codevector y. storage while maintaining an acceptable fidelity or

A New Side-Match Finite-State Vector Qua
✍ Yu-Len Huang; Ruey-Feng Chang 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 310 KB

The side-match finite-state vector quantization (SMVQ) schemes improve performance over the vector quantization by exploiting the neighboring vector correlations within the image. In this paper, we propose a neural network side-match finite-state vector quantization (NN-SMVQ) scheme that combines th