UNQA: Unified No-Reference Quality Assessment for Audio, Image, Video, and Audio-Visual Content
<|reference_start|>UNQA: Unified No-Reference Quality Assessment for Audio, Image, Video, and Audio-Visual Content: As multimedia data flourishes on the Internet, quality assessment (QA) of multimedia data becomes paramount for digital media applications. Since multimedia data includes multiple modalities including audio, image, video, and audio-visual (A/V) content, researchers have developed a range of QA methods to evaluate the quality of different modality data. While they exclusively focus on addressing the single modality QA issues, a unified QA model that can handle diverse media across multiple modalities is still missing, whereas the latter can better resemble human perception behaviour and also have a wider range of applications. In this paper, we propose the Unified No-reference Quality Assessment model (UNQA) for audio, image, video, and A/V content, which tries to train a single QA model across different media modalities. To tackle the issue of inconsistent quality scales among different QA databases, we develop a multi-modality strategy to jointly train UNQA on multiple QA databases. Based on the input modality, UNQA selectively extracts the spatial features, motion features, and audio features, and calculates a final quality score via the four corresponding modality regression modules. Compared with existing QA methods, UNQA has two advantages: 1) the multi-modality training strategy makes the QA model learn more general and robust quality-aware feature representation as evidenced by the superior performance of UNQA compared to state-of-the-art QA methods. 2) UNQA reduces the number of models required to assess multimedia data across different modalities. and is friendly to deploy to practical applications.<|reference_end|>
@article{cao2024unqa:,
    title={UNQA: Unified No-Reference Quality Assessment for Audio, Image, Video,
  and Audio-Visual Content},
    author={Yuqin Cao, Xiongkuo Min, Yixuan Gao, Wei Sun, Weisi Lin, Guangtao Zhai},
    journal={arXiv preprint arXiv:2407.19704},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19704},
    primaryClass={eess.IV cs.MM cs.SD eess.AS}
}