activity
20202026
most citedRecent Standard Development Activities on Video Coding for Machines

31 citations · 68 across the 24 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

eess.IV2024

BVI-UGC: A Video Quality Database for User-Generated Content Transcoding

Zihao Qi, Chen Feng, Fan Zhang +3

In recent years, user-generated content (UGC) has become one of the major video types consumed via streaming networks. Numerous research contributions have focused on assessing its…

eess.IV2024★ 7 cited

AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results

Maksim Smirnov, Aleksandr Gushchin, Anastasia Antsiferova +29

Video quality assessment (VQA) is a crucial task in the development of video compression standards, as it directly impacts the viewer experience. This paper presents the results of…

cs.CV2024

AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

Marcos V. Conde, Saman Zadtootaghaj, Nabajeet Barman +33

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 method…

eess.IV2024

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

Xin Li, Kun Yuan, Yajing Pei +65

This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected da…

cs.CV2024

Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment

Ziyu Shan, Yujie Zhang, Qi Yang +5

No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference, which have achiev…

cs.CV2024★ 1 cited

Transferable Learned Image Compression-Resistant Adversarial Perturbations

Yang Sui, Zhuohang Li, Ding Ding +4

Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. While existing adversarial perturbations are pr…