Blind Prediction of Natural Video Quality

Saad, Michele A.; Bovik, Alan C.; Charrier, Christophe

doi:10.1109/tip.2014.2299154

Cited by 406 publications

(255 citation statements)

References 51 publications

(56 reference statements)

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“…Furthermore, we also use complexity features based on the Natural Scene Statistics (NSS) of a video. NSS have been used in several recent NR VQA methods, such as [1], [5], [6], [17]. In this work, we use NSS to calculate a measure of spatial complexity of a video and the amount of flicker.…”

Section: Video Analysismentioning

confidence: 99%

Video quality assessment and machine learning: Performance and interpretability

Sogaard

Forchhammer

Korhonen

2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

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Abstract-In this work we compare a simple and a complex Machine Learning (ML) method used for the purpose of Video Quality Assessment (VQA). The simple ML method chosen is the Elastic Net (EN), which is a regularized linear regression model and easier to interpret. The more complex method chosen is Support Vector Regression (SVR), which has gained popularity in VQA research. Additionally, we present an ML-based feature selection method. Also, it is investigated how well the methods perform when tested on videos from other datasets. Our results show that content-independent cross-validation performance on a single dataset can be misleading and that in the case of very limited training and test data, especially in regards to different content as is the case for many video datasets, a simple ML approach is the better choice.

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Section: Video Analysismentioning

confidence: 99%

Video quality assessment and machine learning: Performance and interpretability

Sogaard

Forchhammer

Korhonen

2015

2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX)

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“…Nine different medical ultrasound videos sequences compressed using High Efficiency Video Coding (HEVC) [22][23][24] were tested using the new technique. The ultrasound sequences were related to different organs (e.g.…”

Section: Tests With Hevc Sequencesmentioning

confidence: 99%

A visual quality evaluation method for telemedicine applications

Nasr

Martini

2017

Signal Processing: Image Communication

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Ratio (PSNR) and the Structural SIMilarity index metric (SSIM). Comparisons with subjective results interms of Differential Mean Opinion Scores (DMOS) are also presented. We show that the presented method, not needing the original frame to predict the quality, achieves a high correlation with subjective results (more than 0.9) for the different quality metrics used. We also present relationships between the quality derived via the logo and via the original frame and we assess the overhead in data transmission resulting from the compressed logo data and its protection overhead.Index Terms-Visual quality evaluation, medical ultrasound, objective quality metrics, subjective quality metrics, medical quality of service/experience, wireless telemedicine

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“…Natural Scene Statistics (36) [21] Temporal Variation [20] DCT features (6) [20] Motion coherency (1) [20] …”

Section: Perceptual Characteristicsmentioning

confidence: 99%

“…For this preliminary experiment, we used features from a well-known no-reference quality metric [20,21] to describe the perceptual characteristics of the video. We chose to use off-the-shelf features rather than designing new ones because the focus of this work was to unveil the role of context and user features in predicting QoVE, rather than to improve existing descriptors of perceived quality.…”

Section: Perceptual Characteristicsmentioning

confidence: 99%

Towards a comprehensive model for predicting the quality of individual visual experience

et al. 2015

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Recently, a lot of effort has been devoted to estimating the Quality of Visual Experience (QoVE) in order to optimize video delivery to the user. For many decades, existing objective metrics mainly focused on estimating the perceived quality of a video, i.e., the extent to which artifacts due to e.g. compression disrupt the appearance of the video. Other aspects of the visual experience, such as enjoyment of the video content, were, however, neglected. In addition, typically Mean Opinion Scores were targeted, deeming the prediction of individual quality preferences too hard of a problem. In this paper, we propose a paradigm shift, and evaluate the opportunity of predicting individual QoVE preferences, in terms of video enjoyment as well as perceived quality. To do so, we explore the potential of features of different nature to be predictive for a user's specific experience with a video. We consider thus not only features related to the perceptual characteristics of a video, but also to its affective content. Furthermore, we also integrate in our framework the information about the user and use context. The results show that effective feature combinations can be identified to estimate the QoVE from the perspective of both the enjoyment and perceived quality.

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Blind Prediction of Natural Video Quality

Cited by 406 publications

References 51 publications

Video quality assessment and machine learning: Performance and interpretability

Video quality assessment and machine learning: Performance and interpretability

A visual quality evaluation method for telemedicine applications

Towards a comprehensive model for predicting the quality of individual visual experience

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