论文标题
学习改善图像压缩而不更改标准解码器
Learning to Improve Image Compression without Changing the Standard Decoder
论文作者
论文摘要
近年来，我们目睹了对应用深度神经网络（DNN）以提高图像压缩中的利率延伸性能的兴趣越来越大。但是，现有方法要么在解码器侧训练后处理DNN，要么以端到端的方式提出学习图像压缩的学习。这样，解码器中需要训练有素的DNN，从而导致个人计算机和手机中标准图像解码器（例如JPEG）的不兼容。因此，我们建议学习以改善标准解码器的编码性能。在本文中，我们以JPEG为例。具体而言，提出了一种频域预编辑方法来优化DCT系数的分布，旨在促进JPEG压缩。此外，我们建议与预先编辑网络共同学习JPEG量化表。最重要的是，我们不会修改JPEG解码器，因此在使用广泛使用的标准JPEG解码器查看图像时，我们的方法是适用的。该实验验证了我们的方法能够成功地改善JPEG的速率延伸性能，例如PSNR，MS-SSIM和LPIPS。从视觉上看，这转化为更好的整体颜色保留率，尤其是在应用强压缩时。这些代码可在https://github.com/yannickstruempler/learlearnedjpeg上找到。
In recent years we have witnessed an increasing interest in applying Deep Neural Networks (DNNs) to improve the rate-distortion performance in image compression. However, the existing approaches either train a post-processing DNN on the decoder side, or propose learning for image compression in an end-to-end manner. This way, the trained DNNs are required in the decoder, leading to the incompatibility to the standard image decoders (e.g., JPEG) in personal computers and mobiles. Therefore, we propose learning to improve the encoding performance with the standard decoder. In this paper, We work on JPEG as an example. Specifically, a frequency-domain pre-editing method is proposed to optimize the distribution of DCT coefficients, aiming at facilitating the JPEG compression. Moreover, we propose learning the JPEG quantization table jointly with the pre-editing network. Most importantly, we do not modify the JPEG decoder and therefore our approach is applicable when viewing images with the widely used standard JPEG decoder. The experiments validate that our approach successfully improves the rate-distortion performance of JPEG in terms of various quality metrics, such as PSNR, MS-SSIM and LPIPS. Visually, this translates to better overall color retention especially when strong compression is applied. The codes are available at https://github.com/YannickStruempler/LearnedJPEG.
