论文标题
通过神经图像压缩中过度拟合的解码器偏差提高重建质量
Improving The Reconstruction Quality by Overfitted Decoder Bias in Neural Image Compression
论文作者
论文摘要
端到端可训练的型号已在视频和图像上达到了传统手工制作的压缩技术的性能。由于这些模型的参数是在大型训练集中学习的，因此对于要压缩的任何给定图像并不是最佳的。在本文中，我们提出了一个基于实例的解码器偏见子集的微调，以改善重建质量，以换取额外的编码时间和较小的额外信号成本。所提出的方法适用于任何端到端的压缩方法，将最新的神经图像压缩BD率提高了$ 3-5 \％$。
End-to-end trainable models have reached the performance of traditional handcrafted compression techniques on videos and images. Since the parameters of these models are learned over large training sets, they are not optimal for any given image to be compressed. In this paper, we propose an instance-based fine-tuning of a subset of decoder's bias to improve the reconstruction quality in exchange for extra encoding time and minor additional signaling cost. The proposed method is applicable to any end-to-end compression methods, improving the state-of-the-art neural image compression BD-rate by $3-5\%$.
