research-article

Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images

Authors:

Guangtao ZhaiAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 4

Article No.: 96, Pages 1 - 22

https://doi.org/10.1145/3631357

Published: 11 December 2023 Publication History

Abstract

Computer graphics images (CGIs) are artificially generated by means of computer programs and are widely perceived under various scenarios, such as games, streaming media, etc. In practice, the quality of CGIs consistently suffers from poor rendering during production, inevitable compression artifacts during the transmission of multimedia applications, and low aesthetic quality resulting from poor composition and design. However, few works have been dedicated to dealing with the challenge of computer graphics image quality assessment (CGIQA). Most image quality assessment (IQA) metrics are developed for natural scene images (NSIs) and validated on databases consisting of NSIs with synthetic distortions, which are not suitable for in-the-wild CGIs. To bridge the gap between evaluating the quality of NSIs and CGIs, we construct a large-scale in-the-wild CGIQA database consisting of 6,000 CGIs (CGIQA-6k) and carry out the subjective experiment in a well-controlled laboratory environment to obtain the accurate perceptual ratings of the CGIs. Then, we propose an effective deep learning–based no-reference (NR) IQA model by utilizing both distortion and aesthetic quality representation. Experimental results show that the proposed method outperforms all other state-of-the-art NR IQA methods on the constructed CGIQA-6k database and other CGIQA-related databases. The database is released at https://github.com/zzc-1998/CGIQA6K.

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Index Terms

Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 4

April 2024

676 pages

EISSN:1551-6865

DOI:10.1145/3613617

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 11 December 2023

Online AM: 02 November 2023

Accepted: 26 October 2023

Revised: 15 September 2023

Received: 17 July 2023

Published in TOMM Volume 20, Issue 4

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Ma PLiu LXiao CXu D(2025)Omnidirectional Image Quality Assessment With Mutual DistillationIEEE Transactions on Broadcasting10.1109/TBC.2024.350343571:1(264-276)Online publication date: Mar-2025
https://doi.org/10.1109/TBC.2024.3503435
Zhang JZhao DZhang DLv CSong MPeng QWang NXu C(2025)No-reference image quality assessment based on information entropy vision transformerThe Imaging Science Journal10.1080/13682199.2025.2456431(1-15)Online publication date: 31-Jan-2025
https://doi.org/10.1080/13682199.2025.2456431
Zhang ZSun WWu HZhou YLi CChen ZMin XZhai GLin W(2024)GMS-3DQA: Projection-Based Grid Mini-patch Sampling for 3D Model Quality AssessmentACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364381720:6(1-19)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3643817
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