Bilevel Quantization using Dithering and Hopfield theory

Bilevel Quantization using Dithering and Hopfield theory

Bilevel quantization using dither is useful with coarse quantizers. So we study it from statistical viewpoint, furthermore. physical energy theory known as Hopfield neural network or Ising spin system. These are equivalent internally and they should be related to the optimum convergence or minimum quantization error of dithering. In this paper, we show this relationship and theoretical improvements

Abstract Introduction Ststistical interpretation of quantizer error Quantizer noise without and with dither Image halftoning and physical energy considerations Bilevel convergence characteristics Conclusions References

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