论文标题
非线性变换编码
Nonlinear Transform Coding
论文作者
论文摘要
我们审查了一类可根据名称非线性转换编码（NTC）收集的方法，在过去的几年中，这些方法已与图像的最佳线性变换编解码器竞争，并以速率（在既定的感知质量质量指标（例如MS-SSIM））（如MS-SSIM）的速率绩效取代了它们。我们评估了经验率 - 借助于简单的示例来源，NTC的延伸性能是，矢量量化器的最佳性能比自然数据源更容易估计。为此，我们引入了一种新型的熵受限矢量量化的变体。我们为NTC模型提供了各种形式的随机优化技术的分析；审查基于人工神经网络的转换体系结构以及学习的熵模型；并提供多种方法的直接比较，以参数化速率 - 延伸非线性变换的权衡，并引入简化的变换。
We review a class of methods that can be collected under the name nonlinear transform coding (NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate--distortion performance under established perceptual quality metrics such as MS-SSIM. We assess the empirical rate--distortion performance of NTC with the help of simple example sources, for which the optimal performance of a vector quantizer is easier to estimate than with natural data sources. To this end, we introduce a novel variant of entropy-constrained vector quantization. We provide an analysis of various forms of stochastic optimization techniques for NTC models; review architectures of transforms based on artificial neural networks, as well as learned entropy models; and provide a direct comparison of a number of methods to parameterize the rate--distortion trade-off of nonlinear transforms, introducing a simplified one.
