Blind Prediction of Natural Video Quality

We propose a blind (no reference or NR) video quality evaluation model that is nondistortion specific. The approach relies on a spatio-temporal model of video scenes in the discrete cosine transform domain, and on a model that characterizes the type of motion occurring in the scenes, to predict vide...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing Jg. 23; H. 3; S. 1352 - 1365
Hauptverfasser: Saad, Michele A., Bovik, Alan C., Charrier, Christophe
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.03.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Applied sciences
Blinds
Coherence
Computational modeling
Computer Science
Detection, estimation, filtering, equalization, prediction
discrete cosine transform
Discrete cosine transforms
egomotion
Exact sciences and technology
generalized Gaussian
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image Processing
Information, signal and communications theory
Mathematical model
Mathematical models
Motion
Pattern Recognition, Automated - methods
Photography - methods
Prediction algorithms
Predictive models
Quality assessment
Reproducibility of Results
Sensitivity and Specificity
Signal and communications theory
Signal processing
Signal, noise
Statistics
Telecommunications and information theory
Transaction processing
Vectors
Video quality assessment
Video Recording - methods
Natural scene
Video quality assessment
Algorithm
Subjective evaluation
Image quality
generalized Gaussian
Video databases
discrete cosine transform
Coherence
Quality control
Database
Signal processing
Discrete cosine transforms
egomotion
Image evaluation
ISSN:1057-7149, 1941-0042, 1941-0042
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We propose a blind (no reference or NR) video quality evaluation model that is nondistortion specific. The approach relies on a spatio-temporal model of video scenes in the discrete cosine transform domain, and on a model that characterizes the type of motion occurring in the scenes, to predict video quality. We use the models to define video statistics and perceptual features that are the basis of a video quality assessment (VQA) algorithm that does not require the presence of a pristine video to compare against in order to predict a perceptual quality score. The contributions of this paper are threefold. 1) We propose a spatio-temporal natural scene statistics (NSS) model for videos. 2) We propose a motion model that quantifies motion coherency in video scenes. 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. The proposed algorithm, called video BLIINDS, is tested on the LIVE VQA database and on the EPFL-PoliMi video database and shown to perform close to the level of top performing reduced and full reference VQA algorithms.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2014.2299154