&lt;title&gt;Video quality assessment based on data hiding driven by optical flow information&lt;/title&gt;

Farias, Mylène C. Q.; Carli, Marco; Neri, Alessandro; Mitra, S.K.

doi:10.1117/12.527792

Cited by 11 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of data hiding methods for the evaluation of visual quality was proposed in [5], [19], [20]. There, instead of extracting features from the bit-stream or the decoded video, the methods evaluate to which extent an inserted watermark can be recovered at the receiver.…”

Section: A No-reference Quality Metricsmentioning

confidence: 99%

Rule-Based No-Reference Video Quality Evaluation Using Additionally Coded Videos

Oelbaum

Keimel

Diepold

2009

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

Abstract-This contribution presents a no-reference video quality metric, which is based on a set of simple rules that assigns a given video to one of four different content classes. The four content classes distinguish between video sequences which are coded with a very low data rate, which are sensitive to blocking effects, which are sensitive to blurring, and a general model for all other types of video sequences. The appropriate class for a given video sequence is selected based on the evaluation of feature values of an additional low quality version of the given video, which is generated by encoding. The visual quality for a video sequence is estimated using a set of features, which includes measures for the blockiness, the blurriness, the spatial activity and a set of additional continuity features. The way these features are combined to one overall quality value is determined by the feature class, to which the video has been assigned. We also propose an additional correction step for the visual quality value. The proposed metric is verified in a process, which includes visual quality values originating from subjective quality tests in combination with a cross validation approach. The presented metric significantly outperforms PSNR as a visual quality estimator. The Pearson correlation between the estimated visual quality values and the subjective test results takes on values as high as 0.82.

show abstract

Section: A No-reference Quality Metricsmentioning

confidence: 99%

Rule-Based No-Reference Video Quality Evaluation Using Additionally Coded Videos

Oelbaum

Keimel

Diepold

2009

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Quality assessment is achieved using objective or subjective methods. There are generally three methodologies for objective video quality assessment with three levels of measurement accuracy: Full-reference (FR) metrics, Reduced-reference (RR) metrics and No-reference (NR) metrics [1,2]. NR metrics which do not depend on the source video are widely required in many systems and applications.…”

Section: Introductionmentioning

confidence: 99%

The Research of Video Quality Evaluating Based on MATLAB

Chen

Yao

2015

AMM

View full text Add to dashboard Cite

Video quality impairments mainly caused by noisy transmit channel. No-Reference evaluating method is an important way to predict the video quality due to without any other loader to the transmit channel. Aiming at improve the accuracy and effectiveness of No-Reference video quality evaluating model, a novel variable-weight evaluating model is proposed. Motion intensity is introduced to qualify the variation of video content. And a weight control function is obtained based on the statistic studying method. Then the weight of clearness and smoothness can be adjusted in real time through the weight control function. The variable-weight evaluating model takes into the human perceptual properties. The experimental results show that the variable-weight evaluation model outperforms the existing evaluation models and achieves high correlation with the subjective test results.

show abstract

“…Most of existing approaches to QA of video focus on the distortion caused by compression [2,3] and transmission [4]. The quality of video itself is not assessed when it's captured.…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment of User-Generated Video Using Camera Motion

Guo

Gurrin

Hopfgartner

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. With user-generated video (UGV) becoming so popular on the Web, the availability of a reliable quality assessment (QA) measure of UGV is necessary for improving the users' quality of experience in videobased application. In this paper, we explore QA of UGV based on how much irregular camera motion it contains with low-cost manner. A blockmatch based optical flow approach has been employed to extract camera motion features in UGV, based on which, irregular camera motion is calculated and automatic QA scores are given. Using a set of UGV clips from benchmarking datasets as a showcase, we observe that QA scores from the proposed automatic method and subjective method fit well. Further, the automatic method reports much better performance than the random run. These confirm the satisfaction of the automatic QA scores indicating the quality of the UGV when only considering visual camera motion. Furthermore, it also shows that the UGV quality can be assessed automatically for improving the end users quality of experience in video-based applications.

show abstract

<title>Video quality assessment based on data hiding driven by optical flow information</title>

Cited by 11 publications

References 20 publications

Rule-Based No-Reference Video Quality Evaluation Using Additionally Coded Videos

Rule-Based No-Reference Video Quality Evaluation Using Additionally Coded Videos

The Research of Video Quality Evaluating Based on MATLAB

Quality Assessment of User-Generated Video Using Camera Motion

Contact Info

Product

Resources

About