Video Quality Assessment
94 papers with code • 10 benchmarks • 12 datasets
Video Quality Assessment is a computer vision task aiming to mimic video-based human subjective perception. The goal is to produce a mos score, where higher score indicates better perceptual quality. Some well-known benchmarks for this task are KoNViD-1k, LIVE-VQC, YouTube-UGC and LSVQ. SROCC/PLCC/RMSE are usually used to evaluate the performance of different models.
Benchmarks
These leaderboards are used to track progress in Video Quality Assessment
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Libraries
Use these libraries to find Video Quality Assessment models and implementationsDatasets
LIVE-VQC
YouTube-UGC
MSU SR-QA Dataset
MSU NR VQA Database
MSU FR VQA Database
LIVE-FB LSVQ
LIVE Livestream
Most implemented papers
Multiscale structural similarity for image quality assessment
VainF/pytorch-msssim
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The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers 2004
The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality.
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
Numerous single-image super-resolution algorithms have been proposed in the literature, but few studies address the problem of performance evaluation based on visual perception.
UNIQUE: Unsupervised Image Quality Estimation
A linear decoder is trained with 7 GB worth of data, which corresponds to 100, 000 8x8 image patches randomly obtained from nearly 1, 000 images in the ImageNet 2013 database.
Power of Tempospatially Unified Spectral Density for Perceptual Video Quality Assessment
This is a full-reference tempospatial approach that considers both temporal and spatial PSD characteristics.
Quality Assessment of In-the-Wild Videos
lidq92/VSFA
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We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network.
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets.
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
baidut/PaQ-2-PiQ
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CVPR 2020
Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved problem of great consequence to the social and streaming media industries that impacts billions of viewers daily.
Image Quality Assessment: Unifying Structure and Texture Similarity
Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original.
Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos
We first carry out a psychophysical experiment to investigate the interplay among the VR viewing conditions, the user viewing behaviors, and the perceived quality of 360{\deg} images.
Study on the Assessment of the Quality of Experience of Streaming Video
VQA (Video Quality Assessment) models based on regression and gradient boosting with SRCC reaching up to 0. 9647 on the validation subsample are proposed.
