LSCD: A Large-Scale Screen Content Dataset for Video Compression
Yuhao Cheng Siru Zhang Yiqiang Yan Rong Chen Yun Zhang
Multimedia compression allows us to watch videos, see pictures and hear
sounds within a limited bandwidth, which helps the flourish of the internet.
During the past decades, multimedia compression has achieved great success
using hand-craft features and systems. With the development of artificial
intelligence and video compression, there emerges a lot of research work
related to using the neural network on the video compression task to get rid of
the complicated system. Not only producing the advanced algorithms, but
researchers also spread the compression to different content, such as User
Generated Content(UGC). With the rapid development of mobile devices, screen
content videos become an important part of multimedia data. In contrast, we
find community lacks a large-scale dataset for screen content video
compression, which impedes the fast development of the corresponding
learning-based algorithms. In order to fulfill this blank and accelerate the
research of this special type of videos, we propose the Large-scale Screen
Content Dataset(LSCD), which contains 714 source sequences. Meanwhile, we
provide the analysis of the proposed dataset to show some features of screen
content videos, which will help researchers have a better understanding of how
to explore new algorithms. Besides collecting and post-processing the data to
organize the dataset, we also provide a benchmark containing the performance of
both traditional codec and learning-based methods.