A Convolutional Neural Network Approach for Objective Video Quality Assessment

@article{Callet2006ACN,
  title={A Convolutional Neural Network Approach for Objective Video Quality Assessment},
  author={Patrick Le Callet and Christian Viard-Gaudin and Dominique Barba},
  journal={IEEE Transactions on Neural Networks},
  year={2006},
  volume={17},
  pages={1316-1327}
}
This paper describes an application of neural networks in the field of objective measurement method designed to automatically assess the perceived quality of digital videos. This challenging issue aims to emulate human judgment and to replace very complex and time consuming subjective quality assessment. Several metrics have been proposed in literature to tackle this issue. They are based on a general framework that combines different stages, each of them addressing complex problems. The… Expand
No-reference video quality measurement: added value of machine learning
TLDR
The utility of the ideas are demonstrated by considering the practical scenario of video broadcast transmissions with focus on digital terrestrial television (DTT) and proposing a no-reference objective video quality estimator for such application and conducting meaningful verification studies on different video content to verify the performance of the proposed solution. Expand
Convolutional Neural Networks for Video Quality Assessment
TLDR
A novel Deep Learning (DL) framework is introduced for effectively predicting VQA of video content delivery mechanisms based on end-to-end feature learning, based on Convolutional Neural Networks, taking into account compression distortion as well as transmission delays. Expand
Full Reference Video Quality Measures Improvement Using Neural Networks
TLDR
This work proposes a machine learning based improvement for each of the VQMs considered in this work and a video quality metric fusion index (VQMFI) that jointly exploits all the VQLs considered in the study as well as spatiotemporal features to produce a better estimation of the subjective quality. Expand
Deep Video Quality Assessor: From Spatio-Temporal Visual Sensitivity to a Convolutional Neural Aggregation Network
TLDR
A novel full-reference VQA framework named Deep Video Quality Assessor (DeepVQA) is proposed to quantify the spatio-temporal visual perception via a convolutional neural network (CNN) and a convolved neural aggregation network (CNAN) and to manipulate the temporal variation of distortions. Expand
A Locally Adaptive System for the Fusion of Objective Quality Measures
TLDR
This paper describes the LAF system, an interpretable machine learning system for objective quality assessment, namely the locally adaptive fusion (LAF), and compares its performance with traditional machine learning. Expand
Constructing a No-Reference H.264/AVC Bitstream-Based Video Quality Metric Using Genetic Programming-Based Symbolic Regression
TLDR
A novel no-reference bitstream-based objective video quality metric is presented that is constructed by genetic programming-based symbolic regression and shows that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream. Expand
Perceptual Video Quality Assessment and Enhancement
TLDR
A quality-aware video system which combines spatial and temporal quality measures with a robust video watermarking technique, such that RR-VQA can be performed without transmitting RR features via an ancillary lossless channel, and a novel strategy for enhancing video denoising algorithms, namely poly-view fusion. Expand
High-Quality Visual Experience
TLDR
This chapter analyzes recently proposed reduced/no-reference (RR/NR) VQA algorithms, and summarizes some properties of the human visual system (HVS) that are frequently utilized in developing V QA algorithms. Expand
Full-Reference Video Quality Assessment Using Deep 3D Convolutional Neural Networks
TLDR
The proposed approach estimates the spatio-temporal quality of a video with respect to its pristine version by applying commonly used distance measures such as the l1 or the l2 norm to the volume-wise pristine and distorted 3D ConvNet features. Expand
Photo Quality Assessment with DCNN that Understands Image Well
TLDR
A deep convolutional neural network is adopted that “understands” images well and is proved to be effective in many computer vision problems and it does not need human efforts in the design of features. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 53 REFERENCES
A metric for continuous quality evaluation of compressed video with severe distortions
TLDR
An objective quality metric that generates continuous estimates of perceived quality for low bit rate video is introduced based on a multichannel model of the human visual system that exceeds the performance of a similar metric based on the Mean Squared Error. Expand
Video quality assessment using neural network based on multi-feature extraction
TLDR
After training with the subjective mean opinion scores (MOS) data of VQEG test sequences, the neural network model can be used to evaluate video quality with good correlation performance in terms of accuracy and consistency measurements. Expand
Robust approach for color image quality assessment
  • P. Callet, D. Barba
  • Mathematics, Engineering
  • Visual Communications and Image Processing
  • 2003
TLDR
High performances are obtained leading to assure that the metric is robust, so this approach constitutes an alternative useful tool to PSNR for image quality assessment. Expand
Objective quality assessment of MPEG-2 video streams by using CBP neural networks
TLDR
A methodology using circular backpropagation (CBP) neural networks for the objective quality assessment of motion picture expert group (MPEG) video streams and provides a satisfactory, continuous-time approximation for actual scoring curves, which was validated statistically in terms of confidence analysis. Expand
Objective video quality assessment system based on human perception
TLDR
A perception-based model that predicts subjective ratings from these objective measurements, and a demonstration of the correlation between the model's predictions and viewer panel ratings are presented. Expand
Issues in vision modeling for perceptual video quality assessment
TLDR
This paper discusses issues in vision modeling for perceptual video quality assessment (PVQA), to explain how important characteristics of the human visual system may be incorporated in vision models for PVQA, to give a brief overview of the state-of-the-art and current efforts in this field, and to outline directions for future research. Expand
Video quality assessment based on structural distortion measurement
TLDR
A new philosophy in designing image and video quality metrics is followed, which uses structural dis- tortion as an estimate of perceived visual distortion as part of full-reference (FR) video quality assessment. Expand
An objective measurement tool for MPEG video quality
TLDR
This model has been able to mimic quite accurately the temporally varying subjective picture quality of video sequences as recorded by the ITU-R SSCQE method. Expand
Digital video quality metric based on human vision
TLDR
A new digital video quality metric, which is based on the discrete cosine transform, which incorporates aspects of early visual pro- cessing, including light adaptation, luminance, and chromatic chan- nels; spatial and temporal filtering; spatial frequency channels; con- trast masking; and probability summation. Expand
Objective picture quality scale for video images (PQSvideo): definition of distortion factors
TLDR
The PQSvideo approximates MOS successfully, utilizing the principal component analysis method and the multiple regression analysis method between quantity of each essential distortion factor and MOS (Mean Opinion Score) obtained by assessment test. Expand
...
1
2
3
4
5
...