Lossless high dynamic range image coding based on lifting scheme using nonlinear interpolative effect of discrete-time cellular neural networks
2005
The lifting scheme is a flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, we propose a novel lossless high dynamic range (HDR) image coding method based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs). In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN. Because the output function of DT-CNN works as a multi-level quantization function, our method adapts for the prediction of HDR image, and composes the integer lifting scheme for lossless coding. Moreover, our method makes good use of the nonlinear interpolative dynamics by A-template compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting method using linear filters.
Keywords:
- Wavelet transform
- Artificial intelligence
- Quantization (signal processing)
- Mathematical optimization
- Machine learning
- Lifting scheme
- High dynamic range
- Lossless compression
- Computer science
- Nonlinear system
- Linear filter
- Cellular neural network
- Theoretical computer science
- Discrete time and continuous time
- Pattern recognition
- Correction
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