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20192023
most citedUGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content

313 citations · 1.4k across the 26 of their papers we have counts for

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Showing 2020Show all

10 papers · 1 filter

cs.MM2020★ 46 cited

ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction

Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang +2

We consider the problem of conducting frame rate dependent video quality assessment (VQA) on videos of diverse frame rates, including high frame rate (HFR) videos. More generally,…

eess.IV2020★ 31 cited

Adaptive Debanding Filter

Zhengzhong Tu, Jessie Lin, Yilin Wang +2

Banding artifacts, which manifest as staircase-like color bands on pictures or video frames, is a common distortion caused by compression of low-textured smooth regions. These fals…

cs.MM2020★ 1 cited

Rate distortion optimization over large scale video corpus with machine learning

Sam John, Akshay Gadde, Balu Adsumilli

We present an efficient codec-agnostic method for bitrate allocation over a large scale video corpus with the goal of minimizing the average bitrate subject to constraints on avera…

cs.MM2020★ 66 cited

Subjective and Objective Quality Assessment of High Frame Rate Videos

Pavan C. Madhusudana, Xiangxu Yu, Neil Birkbeck +3

High frame rate (HFR) videos are becoming increasingly common with the tremendous popularity of live, high-action streaming content such as sports. Although HFR contents are genera…

cs.MM2020★ 15 cited

Capturing Video Frame Rate Variations via Entropic Differencing

Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang +2

High frame rate videos are increasingly getting popular in recent years, driven by the strong requirements of the entertainment and streaming industries to provide high quality of…

cs.CV2020★ 313 cited

UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content

Zhengzhong Tu, Yilin Wang, Neil Birkbeck +2

Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer c…

eess.IV2020★ 52 cited

Predicting the Quality of Compressed Videos with Pre-Existing Distortions

Xiangxu Yu, Neil Birkbeck, Yilin Wang +3

Over the past decade, the online video industry has greatly expanded the volume of visual data that is streamed and shared over the Internet. Moreover, because of the increasing ea…

cs.MM2020

Subjective Quality Assessment for YouTube UGC Dataset

Joong Gon Yim, Yilin Wang, Neil Birkbeck +1

Due to the scale of social video sharing, User Generated Content (UGC) is getting more attention from academia and industry. To facilitate compression-related research on UGC, YouT…

eess.IV2020

BBAND Index: A No-Reference Banding Artifact Predictor

Zhengzhong Tu, Jessie Lin, Yilin Wang +2

Banding artifact, or false contouring, is a common video compression impairment that tends to appear on large flat regions in encoded videos. These staircase-shaped color bands can…

cs.MM2020★ 3 cited

A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment

Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen +3

Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores. However, less attention has been paid to studying the…