Large-Scale Study of Perceptual Video Quality
arXiv:1803.01761 · doi:10.1109/TIP.2018.2869673
Abstract
The great variations of videographic skills, camera designs, compression and processing protocols, and displays lead to an enormous variety of video impairments. Current no-reference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain very limited content, fixed resolutions, were captured using a small number of camera devices by a few videographers and have been subjected to a modest number of distortions. As such, these databases fail to adequately represent real world videos, which contain very different kinds of content obtained under highly diverse imaging conditions and are subject to authentic, often commingled distortions that are impossible to simulate. As a result, NR video quality predictors tested on real-world video data often perform poorly. Towards advancing NR video quality prediction, we constructed a large-scale video quality assessment database containing 585 videos of unique content, captured by a large number of users, with wide ranges of levels of complex, authentic distortions. We collected a large number of subjective video quality scores via crowdsourcing. A total of 4776 unique participants took part in the study, yielding more than 205000 opinion scores, resulting in an average of 240 recorded human opinions per video. We demonstrate the value of the new resource, which we call the LIVE Video Quality Challenge Database (LIVE-VQC), by conducting a comparison of leading NR video quality predictors on it. This study is the largest video quality assessment study ever conducted along several key dimensions: number of unique contents, capture devices, distortion types and combinations of distortions, study participants, and recorded subjective scores. The database is available for download on this link: http://live.ece.utexas.edu/research/LIVEVQC/index.html .
References in corpus (1)
Cited by in corpus (24)
- UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
- RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content
- Patch-VQ: 'Patching Up' the Video Quality Problem
- Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training
- Subjective and Objective Quality Assessment of High Frame Rate Videos
- Predicting the Quality of Compressed Videos with Pre-Existing Distortions
- Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach
- ChipQA: No-Reference Video Quality Prediction via Space-Time Chips
- From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
- Helping Visually Impaired People Take Better Quality Pictures
- Advances in Artificial Intelligence: A Review for the Creative Industries
- BVI-VFI: A Video Quality Database for Video Frame Interpolation
- A strong baseline for image and video quality assessment
- Enhancing Blind Video Quality Assessment with Rich Quality-aware Features
- Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos
- Full-reference Video Quality Assessment for User Generated Content Transcoding
- A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment
- Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models
- Highly Efficient No-reference 4K Video Quality Assessment with Full-Pixel Covering Sampling and Training Strategy
- Understanding the Perceived Quality of Video Predictions
- StarVQA: Space-Time Attention for Video Quality Assessment
- RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content
- Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment
- Objective video quality metrics application to video codecs comparisons: choosing the best for subjective quality estimation