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Found 57 papers, 23 papers with code
Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos
We demonstrate the usefulness of the new subjective database by benchmarking objective models of visual quality on it.
Subjective and Objective Analysis of Indian Social Media Video Quality
We conducted a large-scale subjective study of the perceptual quality of User-Generated Mobile Video Content on a set of mobile-originated videos obtained from the Indian social media platform ShareChat.
A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos
Recent years have seen steady growth in the popularity and availability of High Dynamic Range (HDR) content, particularly videos, streamed over the internet.
Bitrate Ladder Construction using Visual Information Fidelity
Over the past few years, a variety of methods have been proposed to construct optimal bitrate ladders including using low-level features to predict cross-over bitrates, optimal resolutions for each bitrate, predicting visual quality, etc.
Joint Deep Image Restoration and Unsupervised Quality Assessment
Deep learning techniques have revolutionized the fields of image restoration and image quality assessment in recent years.
Quality Modeling Under A Relaxed Natural Scene Statistics Model
Information-theoretic image quality assessment (IQA) models such as Visual Information Fidelity (VIF) and Spatio-temporal Reduced Reference Entropic Differences (ST-RRED) have enjoyed great success by seamlessly integrating natural scene statistics (NSS) with information theory.
HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment
Our findings demonstrate that self-supervised pre-trained neural networks on SDR content can be further fine-tuned in a self-supervised setting using limited unlabeled HDR videos to achieve state-of-the-art performance on the only publicly available VQA database for HDR content, the LIVE-HDR VQA database.
Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos
We present the outcomes of a recent large-scale subjective study of Mobile Cloud Gaming Video Quality Assessment (MCG-VQA) on a diverse set of gaming videos.
SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation
Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness.
Helping Visually Impaired People Take Better Quality Pictures
The photographs taken by visually impaired users often suffer from one or both of two kinds of quality issues: technical quality (distortions), and semantic quality, such as framing and aesthetic composition.
GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content
The mobile cloud gaming industry has been rapidly growing over the last decade.
HDR or SDR? A Subjective and Objective Study of Scaled and Compressed Videos
We conducted a large-scale study of human perceptual quality judgments of High Dynamic Range (HDR) and Standard Dynamic Range (SDR) videos subjected to scaling and compression levels and viewed on three different display devices.
Making Video Quality Assessment Models Robust to Bit Depth
We introduce a novel feature set, which we call HDRMAX features, that when included into Video Quality Assessment (VQA) algorithms designed for Standard Dynamic Range (SDR) videos, sensitizes them to distortions of High Dynamic Range (HDR) videos that are inadequately accounted for by these algorithms.
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos
We present a no-reference video quality model and algorithm that delivers standout performance for High Dynamic Range (HDR) videos, which we call HDR-ChipQA.
One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment
The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry.
Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild
To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupervised setting.
Ranked #3 on
No-Reference Image Quality Assessment
on CSIQ
Subjective Assessment of High Dynamic Range Videos Under Different Ambient Conditions
High Dynamic Range (HDR) videos can represent a much greater range of brightness and color than Standard Dynamic Range (SDR) videos and are rapidly becoming an industry standard.
CONVIQT: Contrastive Video Quality Estimator
Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms.
Ranked #1 on
Video Quality Assessment
on LIVE-ETRI
(using extra training data)
Efficient Per-Shot Convex Hull Prediction By Recurrent Learning
Adaptive video streaming relies on the construction of efficient bitrate ladders to deliver the best possible visual quality to viewers under bandwidth constraints.
Making Video Quality Assessment Models Sensitive to Frame Rate Distortions
We consider the problem of capturing distortions arising from changes in frame rate as part of Video Quality Assessment (VQA).
Estimating the Resize Parameter in End-to-end Learned Image Compression
By conducting extensive experimental tests on existing deep image compression models, we show results that our new resizing parameter estimation framework can provide Bj{\o}ntegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
Perceptual Quality Assessment of UGC Gaming Videos
In recent years, with the vigorous development of the video game industry, the proportion of gaming videos on major video websites like YouTube has dramatically increased.
Foveation-based Deep Video Compression without Motion Search
In our learning based approach, we implement foveation by introducing a Foveation Generator Unit (FGU) that generates foveation masks which direct the allocation of bits, significantly increasing compression efficiency while making it possible to retain an impression of little to no additional visual loss given an appropriate viewing geometry.
Subjective and Objective Analysis of Streamed Gaming Videos
A number of studies have been directed towards understanding the perceptual characteristics of professionally generated gaming videos arising in gaming video streaming, online gaming, and cloud gaming.
FUNQUE: Fusion of Unified Quality Evaluators
Fusion-based quality assessment has emerged as a powerful method for developing high-performance quality models from quality models that individually achieve lower performances.
FAVER: Blind Quality Prediction of Variable Frame Rate Videos
Video quality assessment (VQA) remains an important and challenging problem that affects many applications at the widest scales.
Image Quality Assessment using Contrastive Learning
We consider the problem of obtaining image quality representations in a self-supervised manner.
Ranked #2 on
Video Quality Assessment
on LIVE-ETRI
(using extra training data)
Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression
Block based motion estimation is integral to inter prediction processes performed in hybrid video codecs.
High Frame Rate Video Quality Assessment using VMAF and Entropic Differences
In this work we address the problem of frame rate dependent Video Quality Assessment (VQA) when the videos to be compared have different frame rate and compression factor.
Ranked #2 on
Video Quality Assessment
on LIVE-YT-HFR
ChipQA: No-Reference Video Quality Prediction via Space-Time Chips
We propose a new model for no-reference video quality assessment (VQA).
Ranked #1 on
Video Quality Assessment
on LIVE Livestream
Assessment of Subjective and Objective Quality of Live Streaming Sports Videos
Video live streaming is gaining prevalence among video streaming services, especially for the delivery of popular sporting events.
Convolutional Block Design for Learned Fractional Downsampling
The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values.
Space-Time Video Regularity and Visual Fidelity: Compression, Resolution and Frame Rate Adaptation
As a stringent test of the new model, we apply it to the difficult problem of predicting the quality of videos subjected not only to compression, but also to downsampling in space and/or time.
Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models
Video and image quality assessment has long been projected as a regression problem, which requires predicting a continuous quality score given an input stimulus.
On the Space-Time Statistics of Motion Pictures
It is well-known that natural images possess statistical regularities that can be captured by bandpass decomposition and divisive normalization processes that approximate early neural processing in the human visual system.
RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content
However, these models are either incapable or inefficient for predicting the quality of complex and diverse UGC videos in practical applications.
Ranked #4 on
Video Quality Assessment
on LIVE Livestream
A Hitchhiker's Guide to Structural Similarity
The Structural Similarity (SSIM) Index is a very widely used image/video quality model that continues to play an important role in the perceptual evaluation of compression algorithms, encoding recipes and numerous other image/video processing algorithms.
The VIP Gallery for Video Processing Education
Towards enhancing DVP education we have created a carefully constructed gallery of educational tools that is designed to complement a comprehensive corpus of online lectures by providing examples of DVP on real-world content, along with a user-friendly interface that organizes numerous key DVP topics ranging from analog video, to human visual processing, to modern video codecs, etc.
ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction
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.
Ranked #1 on
Video Quality Assessment
on LIVE-YT-HFR
Learning to Compress Videos without Computing Motion
Our framework exploits the regularities inherent to video motion, which we capture by using displaced frame differences as video representations to train the neural network.
Adaptive Debanding Filter
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.
Subjective and Objective Quality Assessment of High Frame Rate Videos
We also conducted a holistic evaluation of existing state-of-the-art Full and No-Reference video quality algorithms, and statistically benchmarked their performance on the new database.
Perceptually Optimizing Deep Image Compression
Mean squared error (MSE) and $\ell_p$ norms have largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties.
Capturing Video Frame Rate Variations via Entropic Differencing
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 experiences to consumers.
Ranked #3 on
Video Quality Assessment
on LIVE-YT-HFR
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
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 capture devices, and the tremendous popularity of social media platforms.
Ranked #11 on
Video Quality Assessment
on YouTube-UGC
BBAND Index: A No-Reference Banding Artifact Predictor
Banding artifact, or false contouring, is a common video compression impairment that tends to appear on large flat regions in encoded videos.
A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment
Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores.
ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression
By building on top of an existing deep image compression model, we are able to demonstrate a bitrate reduction of as much as $31\%$ over MSE optimization, given a specified perceptual quality (VMAF) level.
Speeding up VP9 Intra Encoder with Hierarchical Deep Learning Based Partition Prediction
In VP9 video codec, the sizes of blocks are decided during encoding by recursively partitioning 64$\times$64 superblocks using rate-distortion optimization (RDO).
Adversarial Video Compression Guided by Soft Edge Detection
We propose a video compression framework using conditional Generative Adversarial Networks (GANs).
Large-Scale Study of Perceptual Video Quality
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.
A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction
Recognizing this, we propose a new representation of perceptual image quality, called probabilistic quality representation (PQR), to describe the image subjective score distribution, whereby a more robust loss function can be employed to train a deep BIQA model.
Learning to Predict Streaming Video QoE: Distortions, Rebuffering and Memory
Mobile streaming video data accounts for a large and increasing percentage of wireless network traffic.
Multimedia
Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach
Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images.
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
Towards overcoming these limitations, we designed and created a new database that we call the LIVE In the Wild Image Quality Challenge Database, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices.
Blind Image Quality Assessment
Small Data Image Classification
Blind Prediction of Natural Video Quality
3) We show that the proposed NSS and motion coherency models are appropriate for quality assessment of videos, and we utilize them to design a blind VQA algorithm that correlates highly with human judgments of quality.
Ranked #4 on
Video Quality Assessment
on LIVE-ETRI
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD).
Ranked #6 on
Image Quality Assessment
on MSU FR VQA Database
