Complexity optimized vector quantization: A neural network approach
- Lawrence Livermore National Lab., CA (United States)
- Technische Univ. Muenchen, Garching (Germany). Physikdepartment
We discuss a vector quantization strategy which jointly optimizes distortion errors and complexity costs. A maximum entropy estimation of the vector quantization cost function yields an optimal codebook size, the reference vectors and the assignment frequencies. We compare different complexity measures for the design of image compression algorithms which quantize wavelet decomposed images. An online version of complexity optimized vector quantization is implemented by an artificial neural network with winner-take-all connectivity. Our approach establishes unifying framework for different quantization methods like K-means clustering and its fuzzy version, entropy constrainted vector quantization or self-organizing topological maps and competitive neural networks.
- Research Organization:
- Lawrence Livermore National Lab., CA (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States); Department of Defense, Washington, DC (United States); German Federal Ministry of Science and Technology (Germany)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 10189003
- Report Number(s):
- UCRL-JC--109361; CONF-9203200--1; ON: DE93001304; CNN: Grant ITR-8800-H1; Grant 88-0274
- Country of Publication:
- United States
- Language:
- English
Similar Records
Data compression using artificial neural networks