LSCD: A Large-Scale Screen Content Dataset for Video Compression

TL;DR

This paper introduces LSCD, a large-scale dataset of screen content videos to facilitate research in video compression, especially for AI-based methods, supported by analysis and benchmark results.

Contribution

The paper presents the first large-scale, publicly available dataset for screen content video compression, including analysis and benchmark comparisons.

Findings

Screen content videos have unique features that influence compression performance.

Traditional codecs and learning-based methods show varying effectiveness on the dataset.

The dataset enables better understanding and development of compression algorithms for screen content videos.

Abstract

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.