UNQA: Unified No-Reference Quality Assessment for Audio, Image, Video, and Audio-Visual Content (2407.19704v1)

Abstract: 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.

• The paper introduces a unified model that integrates quality assessment across diverse media without requiring reference signals.
• It employs a multi-modality training strategy that harmonizes quality scales from various databases to enhance overall performance.
• The approach streamlines multimedia quality evaluation, reducing computational complexity and enabling efficient real-world deployment.

UNQA: A Unified Model for No-Reference Quality Assessment of Multimedia Content

The proliferation of multimedia content on the Internet necessitates efficient and accurate quality assessment (QA) methods for various digital media applications. Traditional QA methods are often restricted to single modalities such as audio, image, video, or combined audio-visual (A/V) content. However, as multimedia consumption increasingly spans these modalities, there is a demand for an integrated approach that can concurrently evaluate multiple forms of media data. The paper "UNQA: Unified No-Reference Quality Assessment for Audio, Image, Video, and Audio-Visual Content" introduces a novel framework aimed at addressing this gap.

Overview of the UNQA Model

The UNQA model is a unified no-reference QA system capable of evaluating the quality of audio, image, video, and A/V content. This system leverages a multi-modality training strategy, designed to overcome the challenge of varying quality scales across different QA databases, making it possible to train a single model applicable to diverse media modalities. Unlike traditional models that are modality-specific, UNQA seeks to emulate human perceptual behavior more accurately and extend its usability across various applications.

Key Features of the UNQA Model:

1. Modality-Specific Feature Extraction: UNQA includes distinct modules designed to extract spatial features from images and video frames, motion features from video sequences, and auditory features from audio tracks. This segmentation ensures that the model adequately captures the unique quality features inherent to each media type.
2. Multi-Modal Training Strategy: A core advancement presented is the multi-modality training approach. By training UNQA on multiple QA databases, each representing different modalities, the model learns a robust and comprehensive representation of quality, evident in its enhanced performance compared to state-of-the-art QA methods.
3. Unified and Versatile Application: Implementing UNQA reduces the need for multiple QA models, thereby simplifying deployment in practical scenarios and saving computational resources. This is particularly advantageous for deployment in edge computing environments with limited memory capacity.

Comparative Performance and Significance

The UNQA model demonstrates superior performance across a variety of databases compared to existing QA models. This is reflective not only of its robustness in feature representation but also the effectiveness of its training strategy, which integrates relative ranking to normalize quality assessments across disparate database scales.

The model's ability to handle multimedia data with missing modalities further underscores its potential applicability in dynamic and complex content landscapes such as streaming platforms and digital media archiving, where uniform quality measures are critical.

Implications and Future Directions

The development of UNQA signifies progress toward a more cohesive approach to multimedia QA by providing a model that consistently assesses quality across diverse content types without requiring separate models for each modality. This unified perspective offers substantial implications for the future of multimedia management, enabling streamlined processes for content delivery, user content generation quality assurance, and automated classification systems.

Future research can expand upon the model's capabilities, optimizing it for real-time applications and further refining its adaptability to emerging media formats and consumption patterns. Additionally, exploring hybrid approaches that incorporate reference information where available or integrating deeper semantic comprehension could enhance the model's flexibility and accuracy.

In conclusion, UNQA represents a strategic advancement in multimedia quality assessment, offering a unified solution that aligns more closely with how humans perceive media content and paving the way for more efficient, scalable, and adaptable QA methodologies.