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  • Association for Computing Machinery (ACM)  (2)
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  • Association for Computing Machinery (ACM)  (2)
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  • 1
    Online Resource
    Online Resource
    GMS-3DQA: Projection-Based Grid Mini-patch Sampling for 3D Model Quality Assessment
    Association for Computing Machinery (ACM) ; 2024
    In:  ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 20, No. 6 ( 2024-06-30), p. 1-19
    Details
    In: ACM Transactions on Multimedia Computing, Communications, and Applications, Association for Computing Machinery (ACM), Vol. 20, No. 6 ( 2024-06-30), p. 1-19
    Abstract: Nowadays, most three-dimensional model quality assessment (3DQA) methods have been aimed at improving accuracy. However, little attention has been paid to the computational cost and inference time required for practical applications. Model-based 3DQA methods extract features directly from the 3D models, which are characterized by their high degree of complexity. As a result, many researchers are inclined towards utilizing projection-based 3DQA methods. Nevertheless, previous projection-based 3DQA methods directly extract features from multi-projections to ensure quality prediction accuracy, which calls for more resource consumption and inevitably leads to inefficiency. Thus, in this article, we address this challenge by proposing a no-reference (NR) projection-based G rid M ini-patch S ampling 3D Model Q uality A ssessment (GMS-3DQA) method. The projection images are rendered from six perpendicular viewpoints of the 3D model to cover sufficient quality information. To reduce redundancy and inference resources, we propose a multi-projection grid mini-patch sampling strategy (MP-GMS), which samples grid mini-patches from the multi-projections and forms the sampled grid mini-patches into one quality mini-patch map (QMM). The Swin-Transformer tiny backbone is then used to extract quality-aware features from the QMMs. The experimental results show that the proposed GMS-3DQA outperforms existing state-of-the-art NR-3DQA methods on the point cloud quality assessment databases for both accuracy and efficiency. The efficiency analysis reveals that the proposed GMS-3DQA requires far less computational resources and inference time than other 3DQA competitors. The code is available at https://github.com/zzc-1998/GMS-3DQA .
    Type of Medium: Online Resource
    ISSN: 1551-6857 , 1551-6865
    URL: Article
    DOI: 10.1145/3643817
    RVK:
    ST 325
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2024
    detail.hit.zdb_id: 2182650-X
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images
    Association for Computing Machinery (ACM) ; 2024
    In:  ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 20, No. 4 ( 2024-04-30), p. 1-22
    Details
    In: ACM Transactions on Multimedia Computing, Communications, and Applications, Association for Computing Machinery (ACM), Vol. 20, No. 4 ( 2024-04-30), p. 1-22
    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 .
    Type of Medium: Online Resource
    ISSN: 1551-6857 , 1551-6865
    URL: Article
    DOI: 10.1145/3631357
    RVK:
    ST 325
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2024
    detail.hit.zdb_id: 2182650-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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