论文标题
减少端到端图像压缩中熵瓶颈的摊销间隙
Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression
论文作者
论文摘要
端到端的深层训练模型将超过视频和图像上传统手工制作的压缩技术的性能。核心思想是学习一个非线性转换，以深度神经网络建模，将输入图像映射到潜在空间中，并与潜在分布的熵模型共同映射到潜在的空间中。解码器也被学到了一个可训练的深层网络，重建的图像可以测量失真。这些方法强迫潜在遵循一些先前的分布。由于这些先验是通过在整个训练组中优化学习的，因此性能平均是最佳的。但是，它不能完全适合每个新实例，因此可以通过扩大位流来损害压缩性能。在本文中，我们提出了一种简单但有效的基于实例的参数化方法，以较小的成本减少此摊销差距。所提出的方法适用于任何端到端的压缩方法，将压缩比特率提高了1％，而不会对重建质量产生任何影响。
End-to-end deep trainable models are about to exceed the performance of the traditional handcrafted compression techniques on videos and images. The core idea is to learn a non-linear transformation, modeled as a deep neural network, mapping input image into latent space, jointly with an entropy model of the latent distribution. The decoder is also learned as a deep trainable network, and the reconstructed image measures the distortion. These methods enforce the latent to follow some prior distributions. Since these priors are learned by optimization over the entire training set, the performance is optimal in average. However, it cannot fit exactly on every single new instance, hence damaging the compression performance by enlarging the bit-stream. In this paper, we propose a simple yet efficient instance-based parameterization method to reduce this amortization gap at a minor cost. The proposed method is applicable to any end-to-end compressing methods, improving the compression bitrate by 1% without any impact on the reconstruction quality.
