UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment

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: https://github.com/xinyiW915/SPIE-2023-Supplementary

Filling the gaps in video transcoder deployment in the cloud

Cloud-based deployment of content production and broadcast workflows has continued to disrupt the industry after the pandemic. The key tools required for unlocking cloud workflows, e.g., transcoding, metadata parsing, and streaming playback, are increasingly commoditized. However, as video traffic continues to increase there is a need to consider tools which offer opportunities for further bitrate/quality gains as well as those which facilitate cloud deployment. In this paper we consider preprocessing, rate/distortion optimisation and cloud cost prediction tools which are only just emerging from the research community. These tools are posed as part of the per-clip optimisation approach to transcoding which has been adopted by large streaming media processing entities but has yet to be made more widely available for the industry.Comment: Camera-ready version of BEIT Conference at NAB 202

Video compression dataset and benchmark of learning-based video-quality metrics

Video-quality measurement is a critical task in video processing. Nowadays, many implementations of new encoding standards - such as AV1, VVC, and LCEVC - use deep-learning-based decoding algorithms with perceptual metrics that serve as optimization objectives. But investigations of the performance of modern video- and image-quality metrics commonly employ videos compressed using older standards, such as AVC. In this paper, we present a new benchmark for video-quality metrics that evaluates video compression. It is based on a new dataset consisting of about 2,500 streams encoded using different standards, including AVC, HEVC, AV1, VP9, and VVC. Subjective scores were collected using crowdsourced pairwise comparisons. The list of evaluated metrics includes recent ones based on machine learning and neural networks. The results demonstrate that new no-reference metrics exhibit a high correlation with subjective quality and approach the capability of top full-reference metrics.Comment: 10 pages, 4 figures, 6 tables, 1 supplementary materia

Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment

Video quality assessment (VQA) has attracted growing attention in recent years. While the great expense of annotating large-scale VQA datasets has become the main obstacle for current deep-learning methods. To surmount the constraint of insufficient training data, in this paper, we first consider the complete range of video distribution diversity (\ie content, distortion, motion) and employ diverse pretrained models (\eg architecture, pretext task, pre-training dataset) to benefit quality representation. An Adaptive Diverse Quality-aware feature Acquisition (Ada-DQA) framework is proposed to capture desired quality-related features generated by these frozen pretrained models. By leveraging the Quality-aware Acquisition Module (QAM), the framework is able to extract more essential and relevant features to represent quality. Finally, the learned quality representation is utilized as supplementary supervisory information, along with the supervision of the labeled quality score, to guide the training of a relatively lightweight VQA model in a knowledge distillation manner, which largely reduces the computational cost during inference. Experimental results on three mainstream no-reference VQA benchmarks clearly show the superior performance of Ada-DQA in comparison with current state-of-the-art approaches without using extra training data of VQA.Comment: 10 pages, 5 figures, to appear in ACM MM 202