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AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
Marcos V. Conde;Saman Zadtootaghaj;Nabajeet Barman;Radu Timofte;Chenlong He;Qi Zheng;Ruoxi Zhu;Zhengzhong Tu;Haiqiang Wang;Xiangguang Chen;Wenhui Meng;Xiang Pan;Huiying Shi;Han Zhu;Xiaozhong Xu;Lei Sun;Zhenzhong Chen;Shan Liu;Zicheng Zhang;Haoning Wu;Yingjie Zhou;Chunyi Li;Xiaohong Liu;Weisi Lin;Guangtao Zhai;Wei Sun;Yuqin Cao;Yanwei Jiang;Jun Jia;Zhichao Zhang;Zijian Chen;Weixia Zhang;Xiongkuo Min;Steve Göring;Zihao Qi;Chen Feng
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (5)
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed met...Show More
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
Marcos V. Conde;Saman Zadtootaghaj;Nabajeet Barman;Radu Timofte;Chenlong He;Qi Zheng;Ruoxi Zhu;Zhengzhong Tu;Haiqiang Wang;Xiangguang Chen;Wenhui Meng;Xiang Pan;Huiying Shi;Han Zhu;Xiaozhong Xu;Lei Sun;Zhenzhong Chen;Shan Liu;Zicheng Zhang;Haoning Wu;Yingjie Zhou;Chunyi Li;Xiaohong Liu;Weisi Lin;Guangtao Zhai;Wei Sun;Yuqin Cao;Yanwei Jiang;Jun Jia;Zhichao Zhang;Zijian Chen;Weixia Zhang;Xiongkuo Min;Steve Göring;Zihao Qi;Chen Feng
Year: 2024 | Conference Paper |
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
Xin Li;Kun Yuan;Yajing Pei;Yiting Lu;Ming Sun;Chao Zhou;Zhibo Chen;Radu Timofte;Wei Sun;Haoning Wu;Zicheng Zhang;Jun Jia;Zhichao Zhang;Linhan Cao;Qiubo Chen;Xiongkuo Min;Weisi Lin;Guangtao Zhai;JianHui Sun;Tianyi Wang;Lei Li;Han Kong;Wenxuan Wang;Bing Li;Cheng Luo;Haiqiang Wang;Xiangguang Chen;Wenhui Meng;Xiang Pan;Huiying Shi;Han Zhu;Xiaozhong Xu;Lei Sun;Zhenzhong Chen;Shan Liu;Fangyuan Kong;Haotian Fan;Yifang Xu;Haoran Xu;Mengduo Yang;Jie Zhou;Jiaze Li;Shijie Wen;Mai Xu;Da Li;Shunyu Yao;Jiazhi Du;Wangmeng Zuo;Zhibo Li;Shuai He;Anlong Ming;Huiyuan Fu;Huadong Ma;Yong Wu;Fie Xue;Guozhi Zhao;Lina Du;Jie Guo;Yu Zhang;Huimin Zheng;Junhao Chen;Yue Liu;Dulan Zhou;Kele Xu;Qisheng Xu;Tao Sun;Zhixiang Ding;Yuhang Hu
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Cited by: Papers (20)
This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. Th...Show More
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
Xin Li;Kun Yuan;Yajing Pei;Yiting Lu;Ming Sun;Chao Zhou;Zhibo Chen;Radu Timofte;Wei Sun;Haoning Wu;Zicheng Zhang;Jun Jia;Zhichao Zhang;Linhan Cao;Qiubo Chen;Xiongkuo Min;Weisi Lin;Guangtao Zhai;JianHui Sun;Tianyi Wang;Lei Li;Han Kong;Wenxuan Wang;Bing Li;Cheng Luo;Haiqiang Wang;Xiangguang Chen;Wenhui Meng;Xiang Pan;Huiying Shi;Han Zhu;Xiaozhong Xu;Lei Sun;Zhenzhong Chen;Shan Liu;Fangyuan Kong;Haotian Fan;Yifang Xu;Haoran Xu;Mengduo Yang;Jie Zhou;Jiaze Li;Shijie Wen;Mai Xu;Da Li;Shunyu Yao;Jiazhi Du;Wangmeng Zuo;Zhibo Li;Shuai He;Anlong Ming;Huiyuan Fu;Huadong Ma;Yong Wu;Fie Xue;Guozhi Zhao;Lina Du;Jie Guo;Yu Zhang;Huimin Zheng;Junhao Chen;Yue Liu;Dulan Zhou;Kele Xu;Qisheng Xu;Tao Sun;Zhixiang Ding;Yuhang Hu
Year: 2024 | Conference Paper |
Improvements of the BD-Rate Metrics Using Monotonic Curve-Fitting Methods
Haiqiang Wang;Xin Zhao;Ding Ding;Xiang Pan;Zizheng Liu;Xiaozhong Xu;Shan Liu
2024 Picture Coding Symposium (PCS)
Year: 2024 | Conference Paper |
The Bj⊘ntegaard Delta rate (BD-rate) measurements have been used as the primary metrics to evaluate performance of video codecs. However, current BD-rate calculation methods are only applicable under the condition that the rate-distortion (R-D) values maintain a monotonic relationship, as this prerequisite is essential for computing integral along the distortion axis. To address this limitation, w...Show More
TSMD: A Database for Static Color Mesh Quality Assessment Study
2023 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Year: 2023 | Conference Paper |
Cited by: Papers (5)
Static meshes with texture map are widely used in modern industrial and manufacturing sectors, attracting considerable attention in the mesh compression community due to its huge amount of data. To facilitate the study of static mesh compression algorithm and objective quality metric, we create the Tencent – Static Mesh Dataset (TSMD) containing 42 reference meshes with rich visual characteristics...Show More
Frequency-Assisted Adaptive Sharpening Scheme Considering Bitrate and Quality Tradeoff
Yingxue Pang;Shijie Zhao;Haiqiang Wang;Gen Zhan;Junlin Li;Li Zhang
2023 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2023 | Conference Paper |
Sharpening is a widely adopted technique to improve video quality, which can effectively emphasize textures and alleviate blurring. However, increasing the sharpening level comes with a higher video bitrate, resulting in degraded Quality of Service (QoS). Furthermore, the video quality does not necessarily improve with increasing sharpening levels, leading to issues such as over-sharpening. Clearl...Show More
NTIRE 2022 Challenge on Perceptual Image Quality Assessment
Jinjin Gu;Haoming Cai;Chao Dong;Jimmy S. Ren;Radu Timofte;Yuan Gong;Shanshan Lao;Shuwei Shi;Jiahao Wang;Sidi Yang;Tianhe Wu;Weihao Xia;Yujiu Yang;Mingdeng Cao;Cong Heng;Lingzhi Fu;Rongyu Zhang;Yusheng Zhang;Hao Wang;Hongjian Song;Jing Wang;Haotian Fan;Xiaoxia Hou;Ming Sun;Mading Li;Kai Zhao;Kun Yuan;Zishang Kong;Mingda Wu;Chuanchuan Zheng;Marcos V. Conde;Maxime Burchi;Longtao Feng;Tao Zhang;Yang Li;Jingwen Xu;Haiqiang Wang;Yiting Liao;Junlin Li;Kele Xu;Tao Sun;Yunsheng Xiong;Abhisek Keshari;Komal Komal;Sadbhawana Thakur;Vinit Jakhetiya;Badri N Subudhi;Hao-Hsiang Yang;Hua-En Chang;Zhi-Kai Huang;Wei-Ting Chen;Sy-Yen Kuo;Saikat Dutta;Sourya Dipta Das;Nisarg A. Shah;Anil Kumar Tiwari
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (36)
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual image processing algorithms. The output images of these algorithms have completely different characteristics fro...Show More
NTIRE 2022 Challenge on Perceptual Image Quality Assessment
Jinjin Gu;Haoming Cai;Chao Dong;Jimmy S. Ren;Radu Timofte;Yuan Gong;Shanshan Lao;Shuwei Shi;Jiahao Wang;Sidi Yang;Tianhe Wu;Weihao Xia;Yujiu Yang;Mingdeng Cao;Cong Heng;Lingzhi Fu;Rongyu Zhang;Yusheng Zhang;Hao Wang;Hongjian Song;Jing Wang;Haotian Fan;Xiaoxia Hou;Ming Sun;Mading Li;Kai Zhao;Kun Yuan;Zishang Kong;Mingda Wu;Chuanchuan Zheng;Marcos V. Conde;Maxime Burchi;Longtao Feng;Tao Zhang;Yang Li;Jingwen Xu;Haiqiang Wang;Yiting Liao;Junlin Li;Kele Xu;Tao Sun;Yunsheng Xiong;Abhisek Keshari;Komal Komal;Sadbhawana Thakur;Vinit Jakhetiya;Badri N Subudhi;Hao-Hsiang Yang;Hua-En Chang;Zhi-Kai Huang;Wei-Ting Chen;Sy-Yen Kuo;Saikat Dutta;Sourya Dipta Das;Nisarg A. Shah;Anil Kumar Tiwari
Year: 2022 | Conference Paper |
Layer-Wise Geometry Aggregation Framework for Lossless LiDAR Point Cloud Compression
Point cloud compression is critical to deploy 3D applications like autonomous driving. However, LiDAR point clouds contain many disconnected regions, where redundant bits for unoccupied 3D space and weak correlations between points make it a troublesome problem to achieve efficient compression. This paper aims to aggregate LiDAR point clouds to get compact representations with full consideration o...Show More
No-Reference Deep Quality Assessment of Compressed Light Field Images
2021 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
Unlike traditional 2D image quality assessment, the structural relationship among sub-aperture images (SAIs) is an essential factor affecting the quality evaluation of light field (LF) images, where the labeled datasets are also not sufficient for improving learning performances. To solve these problems, we present a novel deep neural network-based approach to accurately predict the quality of com...Show More
Deep Neural Networks for End-to-End Spatiotemporal Video Quality Prediction and Aggregation
2021 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
We propose an end-to-end deep neural network-based approach for full-reference video quality assessment (VQA). Many VQA methods predict local quality first and then apply a pooling mechanism to get a global score. However, these two steps are mostly independent of each other thus could not consider spatial and temporal information of a video simultaneously. The proposed method combines local quali...Show More
Nested Error Map Generation Network for No-Reference Image Quality Assessment
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
We propose a multi-task learning neural network for No-Reference image quality assessment (NR-IQA). The pro-posed architecture consists of a backbone feature extractor, a nested multi-task generative module and a quality regression module. We adopt a coarse-to-fine strategy to predict objective error maps in two subtasks optimized with different loss functions. The network is designed to be nested...Show More
C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network
Munan Xu;Junming Chen;Haiqiang Wang;Shan Liu;Ge Li;Zhiqiang Bai
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Cited by: Papers (40)
Traditional video quality assessment (VQA) methods evaluate localized picture quality and video score is predicted by temporally aggregating frame scores. However, video quality exhibits different characteristics from static image quality due to the existence of temporal masking effects. In this paper, we present a novel architecture, namely C3DVQA, that uses Convolutional Neural Network with 3D k...Show More
Satisfied-User-Ratio Modeling for Compressed Video
With explosive increase of internet video services, perceptual modeling for video quality has attracted more attentions to provide high quality-of-experience (QoE) for end-users subject to bandwidth constraints, especially for compressed video quality. In this paper, a novel perceptual model for satisfied-user-ratio (SUR) on compressed video quality is proposed by exploiting compressed video bitra...Show More
No-Reference Image Sharpness Assessment Based on Rank Learning
Yabin Zhang;Haiqiang Wang;Fengfeng Tan;Wenjun Chen;Zurong Wu
2019 IEEE International Conference on Image Processing (ICIP)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
To address the label shortage problem and the fixed-size input constraint of CNN models, we propose a no-reference image sharpness assessment method based on rank learning and effective patch extraction. First, we train a Siamese mobilenet network by learning quality ranks among the synthetically blurred and unsharpen seed images without any human label, which provides effective prior knowledge ab...Show More
Prediction of Satisfied User Ratio for Compressed Video
Haiqiang Wang;Ioannis Katsavounidis;Qin Huang;Xin Zhou;C.-C. Jay Kuo
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2018 | Conference Paper |
Cited by: Papers (27)
A large-scale video quality dataset called the VideoSet has been constructed recently to measure human subjective experience of H.264 coded video in terms of the just-noticeable-difference (JND). It measures the first three JND points of 5-second video of resolution 1080p, 720p, 540p and 360p. Based on the VideoSet, we propose a method to predict the satisfied-user-ratio (SUR) curves using a machi...Show More
The just-noticeable-difference (JND) visual perception property has received much attention in characterizing human subjective viewing experience of compressed video. In this work, we quantity the JND-based video quality assessment model using the satisfied user ratio (SUR) curve, and show that the SUR model can be greatly simplified since the JND points of multiple subjects for the same content i...Show More
Measure and Prediction of HEVC Perceptually Lossy/Lossless Boundary QP Values
Qin Huang;Haiqiang Wang;Sung Chang Lim;Hui Yong Kim;Se Yoon Jeong;C.-C. Jay Kuo
Evaluation of coding efficiency is traditionally modeled as a continuous rate-distortion (R-D) function, where the peak signal-to-noise ratio (PSNR) is adopted as the quality measure. Although the PSNR-versus-bitrate curve offers some useful tradeoff information between video quality and coding bit-rates, it does not take human perceptual experience into account. In this work, by following the rec...Show More
MCL-JCV: A JND-based H.264/AVC video quality assessment dataset
Haiqiang Wang;Weihao Gan;Sudeng Hu;Joe Yuchieh Lin;Lina Jin;Longguang Song;Ping Wang;Ioannis Katsavounidis;Anne Aaron;C.-C. Jay Kuo
2016 IEEE International Conference on Image Processing (ICIP)
Year: 2016 | Conference Paper |
Cited by: Papers (162)
A compressed video quality assessment dataset based on the just noticeable difference (JND) model, called MCL-JCV, is recently constructed and released. In this work, we explain its design objectives, selected video content and subject test procedures. Then, we conduct statistical analysis on collected JND data. We compute the difference between every two adjacent JND points and propose an outlier...Show More
A GMM-based stair quality model for human perceived JPEG images
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2016 | Conference Paper |
Cited by: Papers (19)
Based on the notion of just noticeable differences (JND), a stair quality function (SQF) was recently proposed to model human perception on JPEG images. Furthermore, a k-means clustering algorithm was adopted to aggregate JND data collected from multiple subjects to generate a single SQF. In this work, we propose a new method to derive the SQF using the Gaussian Mixture Model (GMM). The newly deri...Show More
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