Neural networks (NN) can improve standard video compression by pre- and post-processing the encoded video. For optimal NN training, the standard codec needs to be replaced with a codec proxy that can provide derivatives of estimated bit-rate and distortion, which are used for gradient back-propagation. Since entropy coding of standard codecs is designed to take into account non-linear dependencies between transform coefficients, bit-rates cannot be well approximated with simple per-coefficient estimators. This paper presents a new approach for bit-rate estimation that is similar to the type employed in training end-to-end neural codecs, and able to efficiently take into account those statistical dependencies. It is defined from a mathematical model that provides closed-form formulas for the estimates and their gradients, reducing the computational complexity. Experimental results demonstrate the method's accuracy in estimating HEVC/H.265 codec bit-rates.
翻译:神经网络(NN) 可以通过预处理和后处理编码视频来改进标准视频压缩。 对于优化 NN 培训,标准代码需要用代码代用器取代,该代用器可以提供估计比特率和扭曲值的衍生物,用于梯度反反向推进。由于标准代码的加密设计是为了考虑到变异系数之间的非线性依赖性,比特率无法与简单的人均效率估计器相近。本文为比特率估算提供了一种新的方法,它类似于培训端到端神经编码器中所使用的类型,并能有效地考虑到这些统计依赖性。它从数学模型中定义,为估计数及其梯度提供闭式公式,从而降低计算复杂性。实验结果显示这种方法在估算 HEVC/H.265 代码比特率时的准确性。