SCID-Compress900: A Multi-Scene Dataset of 4K and 1080P Screen Content Images for Image Compression Research

摘要

With the rapid growth of digital content and the increasing demand for high-resolution displays, the efficient compression of screen content images characterized by text, graphics, and UI elements has become an important research field. This paper introduces a new dataset named SCID-Compress900 specially designed for image compression research. The dataset consists of 900 high-quality screen content images, including 500 4K images and 400 1080P images. All these images are mainly composed of text/graphics, reflecting typical screen content scenarios such as office documents, software interfaces, and presentation slides. The dataset covers a diverse range of content, including various font sizes, graphic styles, and color modes, providing a comprehensive testbed for compression algorithms. To demonstrate the effectiveness of SCID-Compress900, we conduct benchmark tests using several deep learning-based image compression methods commonly employed by researchers. The experimental results show that SCID-Compress900 can well differentiate the performance of different compression algorithms. Compared with existing datasets, SCID-Compress900 offers higher resolution, larger scale, and more targeted content, making it an ideal resource for developing and evaluating advanced image compression algorithms for screen content. This dataset will not only promote the research and development of screen content compression technology but also contribute to the standardization and optimization of compression algorithms in practical applications. The project is available at https://openi.pcl.ac.cn/OpenDatasets/SCID-Compress900.