Abstract
The traditional image compressors, e.g., BPG and H.266, have achieved great image and video compression quality. Recently, Convolutional Neural Network has been used widely in image compression. We proposed an attention-based convolutional neural network for low bit-rate compression to post-process the output of traditional image compression decoder. Across the experimental results on validation sets, the post-processing module trained by MAE and MS-SSIM losses yields the highest PSNR of 32.10 on average at the bit-rate of 0.15.
Abstract (translated)
传统的图像压缩器，如BPG和H.266，已经取得了很好的图像和视频压缩质量。近年来，卷积神经网络在图像压缩中得到了广泛的应用。提出了一种基于注意的卷积神经网络用于低比特率压缩，对传统图像压缩解码器的输出进行后处理。在验证集的实验结果中，由MAE和MS-SSIM损耗训练的后处理模块在比特率为0.15的情况下平均能产生32.10的最高峰值信噪比。
URL
https://arxiv.org/abs/1905.11045
