论文标题
图像压缩的统一图像预处理框架
A Unified Image Preprocessing Framework For Image Compression
论文作者
论文摘要
随着流媒体技术的开发，沟通的增加取决于声音和视觉信息，这给在线媒体带来了巨大的负担。数据压缩对于减少数据传输和存储的数量变得越来越重要。为了进一步提高图像压缩的效率，研究人员利用各种图像处理方法来补偿常规编解码器和基于先进的基于学习的压缩方法的局限性。我们没有修改面向压缩的方法，而是提出了一个统一的图像压缩预处理框架，称为Kuchen，旨在进一步提高现有编解码器的性能。该框架由混合数据标记系统以及基于学习的骨干形成，以模拟个性化的预处理。据我们所知，这是在图像压缩任务中设置统一的预处理基准测试的第一次探索。结果表明，我们统一的预处理框架优化的现代编解码器不断提高最新压缩的效率。
With the development of streaming media technology, increasing communication relies on sound and visual information, which puts a massive burden on online media. Data compression becomes increasingly important to reduce the volume of data transmission and storage. To further improve the efficiency of image compression, researchers utilize various image processing methods to compensate for the limitations of conventional codecs and advanced learning-based compression methods. Instead of modifying the image compression oriented approaches, we propose a unified image compression preprocessing framework, called Kuchen, which aims to further improve the performance of existing codecs. The framework consists of a hybrid data labeling system along with a learning-based backbone to simulate personalized preprocessing. As far as we know, this is the first exploration of setting a unified preprocessing benchmark in image compression tasks. Results demonstrate that the modern codecs optimized by our unified preprocessing framework constantly improve the efficiency of the state-of-the-art compression.
