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.
翻译:随着媒体流化技术的发展,通信日益依赖于声学和视觉信息,这给在线媒体带来巨大的负担。数据压缩对减少数据传输和存储量越来越重要。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弥补传统编码器和先进的基于学习的压缩方法的局限性。我们提议了一个统一的图像压缩预处理框架,称为Kuchen,目的是进一步改善现有编码器的性能。框架包括一个混合数据标签系统,以及一个模拟个人化预处理的基于学习的骨干。据我们所知,这是第一次探索在图像压缩任务中建立一个统一的预处理基准。结果表明,我们统一的预处理框架所优化的现代编码器不断提高最新技术压缩的效率。