Journal ArticleDOI
A Completely Blind Video Integrity Oracle
TLDR
This work develops a new VQA model called the video intrinsic integrity and distortion evaluation oracle (VIIDEO), which is able to predict the quality of distorted videos without any external knowledge about the pristine source, anticipated distortions, or human judgments of video quality.Abstract:
Considerable progress has been made toward developing still picture perceptual quality analyzers that do not require any reference picture and that are not trained on human opinion scores of distorted images. However, there do not yet exist any such completely blind video quality assessment (VQA) models. Here, we attempt to bridge this gap by developing a new VQA model called the video intrinsic integrity and distortion evaluation oracle (VIIDEO). The new model does not require the use of any additional information other than the video being quality evaluated. VIIDEO embodies models of intrinsic statistical regularities that are observed in natural vidoes, which are used to quantify disturbances introduced due to distortions. An algorithm derived from the VIIDEO model is thereby able to predict the quality of distorted videos without any external knowledge about the pristine source, anticipated distortions, or human judgments of video quality. Even with such a paucity of information, we are able to show that the VIIDEO algorithm performs much better than the legacy full reference quality measure MSE on the LIVE VQA database and delivers performance comparable with a leading human judgment trained blind VQA model. We believe that the VIIDEO algorithm is a significant step toward making real-time monitoring of completely blind video quality possible. The software release of VIIDEO can be obtained online ( http://live.ece.utexas.edu/research/quality/VIIDEO_release.zip ).read more
Citations
More filters
Journal ArticleDOI
Two-Level Approach for No-Reference Consumer Video Quality Assessment
TL;DR: A new approach for learning-based video quality assessment is proposed, based on the idea of computing features in two levels so that low complexity features are computed for the full sequence first, and then high complexity Features are extracted from a subset of representative video frames, selected by using the low complexity Features.
Journal ArticleDOI
Large-Scale Study of Perceptual Video Quality
Zeina Sinno,Alan C. Bovik +1 more
TL;DR: The live video quality challenge database (LIVE-VQC) as mentioned in this paper is 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.
Proceedings ArticleDOI
Quality Assessment of In-the-Wild Videos
TL;DR: This work proposes an objective no-reference video quality assessment method by integrating both effects of content-dependency and temporal-memory effects into a deep neural network, which outperforms five state-of-the-art methods by a large margin.
Journal ArticleDOI
Unified Blind Quality Assessment of Compressed Natural, Graphic, and Screen Content Images
TL;DR: A unified content-type adaptive (UCA) blind image quality assessment model that is applicable across content types and leads to superior performance on the constructed CCT database, and is training-free, implying strong generalizability.
Journal ArticleDOI
SpEED-QA: Spatial Efficient Entropic Differencing for Image and Video Quality
TL;DR: A new family of I/VQA models, which this work calls the spatial efficient entropic differencing for quality assessment (SpEED-QA) model, relies on local spatial operations on image frames and frame differences to compute perceptually relevant image/video quality features in an efficient way.
References
More filters
Proceedings ArticleDOI
Multiscale structural similarity for image quality assessment
TL;DR: This paper proposes a multiscale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions, and develops an image synthesis method to calibrate the parameters that define the relative importance of different scales.
Journal ArticleDOI
No-Reference Image Quality Assessment in the Spatial Domain
TL;DR: Despite its simplicity, it is able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms.
Journal ArticleDOI
Making a “Completely Blind” Image Quality Analyzer
TL;DR: This work has recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed, without any exposure to distorted images.
Journal ArticleDOI
Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.
TL;DR: Evidence is presented that the 2D receptive-field profiles of simple cells in mammalian visual cortex are well described by members of this optimal 2D filter family, and thus such visual neurons could be said to optimize the general uncertainty relations for joint 2D-spatial-2D-spectral information resolution.
Journal ArticleDOI
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
TL;DR: This paper presents results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects and is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image.