UGC quality assessment: exploring the impact of saliency in deep feature-based quality assessment

Xinyi Wang; Angeliki Katsenou; David Bull

doi:10.1117/12.2676136

4 October 2023 UGC quality assessment: exploring the impact of saliency in deep feature-based quality assessment

Xinyi Wang, Angeliki Katsenou, David Bull

Proceedings Volume 12674, Applications of Digital Image Processing XLVI; 1267418 (2023) https://doi.org/10.1117/12.2676136
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

The volume of User Generated Content (UGC) has increased in recent years. The challenge with this type of content is assessing its quality. So far, the state-of-the-art metrics are not exhibiting a very high correlation with perceptual quality. In this paper, we explore state-of-the-art metrics that extract/combine natural scene statistics and deep neural network features. We experiment with these by introducing saliency maps to improve perceptibility. We train and test our models using public datasets, namely, YouTube-UGC and KoNViD-1k. Preliminary results indicate that high correlations are achieved by using only deep features while adding saliency is not always boosting the performance. Our results and code will be made publicly available to serve as a benchmark for the research community and can be found on our project page.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinyi Wang, Angeliki Katsenou, and David Bull "UGC quality assessment: exploring the impact of saliency in deep feature-based quality assessment", Proc. SPIE 12674, Applications of Digital Image Processing XLVI, 1267418 (4 October 2023); https://doi.org/10.1117/12.2676136

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