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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Nonlinear Prediction of Multidimensional Signals via Deep Regression with Applications to Image Coding
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Nonlinear Prediction of Multidimensional Signals via Deep Regression with Applications to Image Coding

机译：基于深度回归的多维信号非线性预测及其在图像编码中的应用

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摘要

Deep convolutional neural networks (DCNN) have enjoyed great successes in many signal processing applications because they can learn complex, non-linear causal relationships from input to output. In this light, DCNNs are well suited for the task of sequential prediction of multidimensional signals, such as images, and have the potential of improving the performance of traditional linear predictors. In this research we investigate how far DCNNs can push the envelop in terms of prediction precision. We propose, in a case study, a two-stage deep regression DCNN framework for nonlinear prediction of two-dimensional image signals. In the first-stage regression, the proposed deep prediction network (PredNet) takes the causal context as input and emits a prediction of the present pixel. Three PredNets are trained with the regression objectives of minimizing l1, l2 and l∞ norms of prediction residuals, respectively. The second-stage regression combines the outputs of the three PredNets to generate an even more precise and robust prediction. The proposed deep regression model is applied to lossless predictive image coding, and it outperforms the state-of-the-art linear predictors by appreciable margin.
机译：深卷积神经网络（DCNN）在许多信号处理应用中都取得了巨大的成功，因为它们可以学习从输入到输出的复杂的非线性因果关系。因此，DCNN非常适合于多维信号（例如图像）的顺序预测任务，并且具有改善传统线性预测器性能的潜力。在这项研究中，我们从预测精度的角度研究了DCNN可以推动包络的程度。在一个案例研究中，我们提出了一个两阶段的深度回归DCNN框架，用于二维图像信号的非线性预测。在第一阶段回归中，建议的深度预测网络（PredNet）将因果上下文作为输入，并发出当前像素的预测。训练了三个PredNet，其回归目标是将最小化 1 ，l 2 和我 ∞ 预测残差的范数分别。第二阶段回归结合了三个PredNet的输出，以生成更精确，更可靠的预测。所提出的深度回归模型适用于无损预测图像编码，并且以可观的幅度优于最新的线性预测器。

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