A content-oriented no-reference perceptual video quality assessment method for computer graphics animation videos

Weizhi XIAN, Mingliang ZHOU, Bin FANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

4 Citations (Scopus)

Abstract

In this paper, we propose a content-oriented no-reference (NR) perceptual video quality assessment (VQA) method for computer graphics (CG) animation videos. First, we extract features in terms of spatiotemporal information and its visual perception from the videos as inputs of our proposed artificial neural network-based VQA model. Second, to facilitate the video quality evaluation, we apply a convolutional neural network (CNN) in the VQA model to generate weight factors for the input features adaptively according to the different types of CG content in videos. Third, we build a subjective CG video quality database for validation of VQA metrics. Experiments demonstrated that our method achieved superior performance in terms of evaluating the quality of CG animation videos. Both the code and proposed database are publicly available at https://github.com/WeizhiXian/CGVQA. The corresponding newly established database is available at https:// pan.baidu.com/s/1_P2ZNrLzJwZfG6xa6tKnDQ (password: cgvq).
Original languageEnglish
Pages (from-to)1731-1746
JournalInformation Sciences
Volume608
Early online date16 Jul 2022
DOIs
Publication statusPublished - Aug 2022
Externally publishedYes

Bibliographical note

This research was supported in part by the National Natural Science Foundation of China under Grant No. 62176027, No. 61876026 and No. 61672443, the Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA), the Hong Kong GRF-RGC General Research Fund under Grant No. 11209819 (CityU 9042816) and Grant No. 11203820 (9042598), the General Program of National Natural Science Foundation of Chongqing under Grant cstc2020jcyj msxmX0790, the Fundamental Research Funds for the Central Universities under Grant 2021CDJJMRH-014, the Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS201905, the Human Resources and Social Security Bureau Project of Chongqing under Grant cx2020073, the Huawei Project under Grant H20210586, the Suzhou Institute of University of Science and Technology of China under Grant H20201528, the Youth Project of Guizhou Education Department under Grant Qian Jiao He KY No. [2017] 359, the University-Level Scientific Research Projects under Grant qnsy2018028, the Guizhou Provincial Education Department Young Science and Technology Talents Development Project under Grant Qian Jiao He KY No. [2020] 210, and the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China under 2018AAA0101301.

Keywords

  • Computer graphics animation videos
  • Convolutional neural network
  • No reference
  • Spatiotemporal features
  • Video quality assessment

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