2021 13th International Conference on Quality of Multimedia Experience (QoMEX)最新文献_第5页

User Behaviour Analysis of Volumetric Video in Augmented Reality 增强现实中体积视频的用户行为分析

2021 13th International Conference on Quality of Multimedia Experience (QoMEX) Pub Date : 2021-06-14 DOI: 10.1109/QoMEX51781.2021.9465456

Emin Zerman, Radhika Kulkarni, A. Smolic

引用次数: 9

A Real Haze Video Database for Haze Level Evaluation 一个用于雾霾等级评价的真实雾霾视频数据库

2021 13th International Conference on Quality of Multimedia Experience (QoMEX) Pub Date : 2021-06-14 DOI: 10.1109/QoMEX51781.2021.9465461

Ying Chu, Guoxing Luo, Fan Chen

{"title":"A Real Haze Video Database for Haze Level Evaluation","authors":"Ying Chu, Guoxing Luo, Fan Chen","doi":"10.1109/QoMEX51781.2021.9465461","DOIUrl":"https://doi.org/10.1109/QoMEX51781.2021.9465461","url":null,"abstract":"Current video quality assessment methods mainly deal with videos with distortions generated in compression or capture process, few researchers focus on perceptual degradation caused by heavy haze in real outdoor scenes. However, assessment of haze level in such application scenario is important. For instance, in the advanced driver assistance system the accurate evaluation of haze level helps to properly release warning signals to drivers, which may avoid latent traffic accidents and save people’s lives. It is necessary to develop studies on haze video quality assessment for real outdoor scenes, and to build benchmark dataset for haze videos. To fill the gap of this benchmark lack, a real haze video database called RHVD was designed and constructed. The dataset contains 403 haze videos captured in real outdoor scenes, which were collected from the Flickr video sharing platform and post-edited in our lab. We conducted subjective quality assessment experiments on the RHVD dataset. Forty non-experts were recruited and asked to rate the haze levels for each of the video in the dataset. The original ratings were screened and normalized to calculate the final subjective scores. The RHVD dataset is free to download from the following website: https://drive.google.com/file/d/1zY0LwJyNB8u1JTAJU2X7ZkiYXsBX7BF/view?usp=sharing, or https://pan.baidu.com/s/11p78zwMXmUHMGDj8WDanPA with password RHVD.","PeriodicalId":160378,"journal":{"name":"2021 13th International Conference on Quality of Multimedia Experience (QoMEX)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116881368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness 图像超分辨率质量评估:结构保真度与统计自然性

2021 13th International Conference on Quality of Multimedia Experience (QoMEX) Pub Date : 2021-05-15 DOI: 10.1109/QoMEX51781.2021.9465479

Wei Zhou, Zhou Wang, Zhibo Chen

{"title":"Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness","authors":"Wei Zhou, Zhou Wang, Zhibo Chen","doi":"10.1109/QoMEX51781.2021.9465479","DOIUrl":"https://doi.org/10.1109/QoMEX51781.2021.9465479","url":null,"abstract":"Single image super-resolution (SISR) algorithms reconstruct high-resolution (HR) images with their low-resolution (LR) counterparts. It is desirable to develop image quality assessment (IQA) methods that can not only evaluate and compare SISR algorithms, but also guide their future development. In this paper, we assess the quality of SISR generated images in a two-dimensional (2D) space of structural fidelity versus statistical naturalness. This allows us to observe the behaviors of different SISR algorithms as a tradeoff in the 2D space. Specifically, SISR methods are traditionally designed to achieve high structural fidelity but often sacrifice statistical naturalness, while recent generative adversarial network (GAN) based algorithms tend to create more natural-looking results but lose significantly on structural fidelity. Furthermore, such a 2D evaluation can be easily fused to a scalar quality prediction. Interestingly, we find that a simple linear combination of a straightforward local structural fidelity and a global statistical naturalness measures produce surprisingly accurate predictions of SISR image quality when tested using public subject-rated SISR image datasets. Code of the proposed SFSN model is publicly available at https://github.con/weizhou-geek/SFSN.","PeriodicalId":160378,"journal":{"name":"2021 13th International Conference on Quality of Multimedia Experience (QoMEX)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129025235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Bias-Aware Loss for Training Image and Speech Quality Prediction Models from Multiple Datasets 多数据集训练图像和语音质量预测模型的偏差感知损失

2021 13th International Conference on Quality of Multimedia Experience (QoMEX) Pub Date : 2021-04-20 DOI: 10.1109/QoMEX51781.2021.9465384

Gabriel Mittag, Saman Zadtootaghaj, Thilo Michael, Babak Naderi, S. Möller

引用次数: 5

Speech Quality Assessment in Crowdsourcing: Comparison Category Rating Method 众包中的语音质量评估:比较类别评定法

2021 13th International Conference on Quality of Multimedia Experience (QoMEX) Pub Date : 2021-04-09 DOI: 10.1109/QoMEX51781.2021.9465465

Babak Naderi, S. Möller, Ross Cutler

{"title":"Speech Quality Assessment in Crowdsourcing: Comparison Category Rating Method","authors":"Babak Naderi, S. Möller, Ross Cutler","doi":"10.1109/QoMEX51781.2021.9465465","DOIUrl":"https://doi.org/10.1109/QoMEX51781.2021.9465465","url":null,"abstract":"Traditionally, Quality of Experience (QoE) for a communication system is evaluated through a subjective test. The most common test method for speech QoE is the Absolute Category Rating (ACR), in which participants listen to a set of stimuli, processed by the underlying test conditions, and rate their perceived quality for each stimulus on a specific scale. The Comparison Category Rating (CCR) is another standard approach in which participants listen to both reference and processed stimuli and rate their quality compared to the other one. The CCR method is particularly suitable for systems that improve the quality of input speech. This paper evaluates an adaptation of the CCR test procedure for assessing speech quality in the crowdsourcing set-up. The CCR method was introduced in the ITU-T Rec. P.800 for laboratory-based experiments. We adapted the test for the crowdsourcing approach following the guidelines from ITU-T Rec. P.800 and P.808. We show that the results of the CCR procedure via crowdsourcing are highly reproducible. We also compared the CCR test results with widely used ACR test procedures obtained in the laboratory and crowdsourcing. Our results show that the CCR procedure in crowdsourcing is a reliable and valid test method.","PeriodicalId":160378,"journal":{"name":"2021 13th International Conference on Quality of Multimedia Experience (QoMEX)","volume":"31 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130572585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4