  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.
