Quality assessment using data hiding on perceptually important areas

Carli, Marco; Farais, M.C.Q.; Gelasca, E.D.; Tedesco, Raffaello; Neri, Alessandro

doi:10.1109/icip.2005.1530613

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…Where ∈ ℛ 1 . Therefore, using a second order expansion, the total number of regression weights needed is 12.…”

Section: Message Predictionmentioning

confidence: 99%

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Data Hiding in MPEG Video Files Using Multivariate Regression and Flexible Macroblock Ordering

Shanableh

2012

IEEE Trans.Inform.Forensic Secur.

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This paper proposes two data hiding approaches using compressed MPEG video. The first approach hides message bits by modulating the quantization scale of a constant bitrate video. A payload of one message bit per macroblock is achieved. A second order multivariate regression is used to find an association between macroblock level feature variables and the values of a hidden message bit. The regression model is then used by the decoder to predict the values of the hidden message bits with very high prediction accuracy. The second approach uses the flexible macroblock ordering feature of H.264/AVC to hide message bits. Macroblocks are assigned to arbitrary slice groups according to the content of the message bits to be hidden. A maximum payload of three message bits per macroblock is achieved. The proposed solutions are analyzed in terms of message extraction accuracy, message payload, excessive bitrate and quality distortion. Comparisons with previous work reveal that the proposed solutions are superior in terms of message payload whilst causing less distortion and compression overhead.

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“…Where ∈ ℛ 1 . Therefore, using a second order expansion, the total number of regression weights needed is 12.…”

Section: Message Predictionmentioning

confidence: 99%

“…Such data hiding techniques can also be used for other purposes. For instance, [1] used data hiding techniques to assess the quality of compressed video in the absence of the original reference. The quality is estimated based on computing the degradations of the extracted hidden message.…”

Section: Introductionmentioning

confidence: 99%

Data Hiding in MPEG Video Files Using Multivariate Regression and Flexible Macroblock Ordering

Shanableh

2012

IEEE Trans.Inform.Forensic Secur.

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“…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.

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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.

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“…Only one single source video is used in [10]. In [11][12][13] results are presented for two videos only, three different videos are used in [14] .…”

Section: Too Small Data Basesmentioning

confidence: 99%

“…Without verifying a new metric on previously unknown data, the danger of having a metric that is fitted to special videos is quite high. Unfortunately, the calibration and verification data is not always separated explicitly [10,15,19,20,22,23,[34][35][36][37][38][39][40][41][42].…”

Section: Cross Validationmentioning

confidence: 99%

Improving the verification process of video quality metrics

Keimel

Oelbaum

Diepold

2009

2009 International Workshop on Quality of Multimedia Experience

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The most important step in the development process of a video quality metric is its verification with regards to the subjective quality experience. Even though guidelines in the form of standards and recommendations are well known, there are still quite often shortcomings in the verification process of many metrics. In this contribution we revisit these rules, point out important details and review contributions to video quality metrics for typical shortcomings. We will highlight in detail five steps that should be followed in order to improve the overall quality of the verification process of video quality metrics: using a large and diverse data base, planing and conducting subjective tests carefully, using different data for calibration and verification of a metric, avoiding unnecessary data fitting steps, and a clear and meaningful presentation of the results. Also we will provide short examples how an improper verification might affect the results of a video quality metric.

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Quality assessment using data hiding on perceptually important areas

Cited by 15 publications

References 10 publications

Data Hiding in MPEG Video Files Using Multivariate Regression and Flexible Macroblock Ordering

Data Hiding in MPEG Video Files Using Multivariate Regression and Flexible Macroblock Ordering

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

Improving the verification process of video quality metrics

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