A Statistical Analysis of MPEG-2 Picture Quality for Television Broadcasting

Nakasu, Eisuke; Aoki, Katsunori; Yajima, Ryoichi; Kanatsugu, Yasuaki; Kubota, Keiichi

doi:10.5594/j06443

Cited by 17 publications

(5 citation statements)

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“…This experiment used 13 SDTV and 13 HDTV video sequences covering a wide range of criticality [29] and resulting in a wide distribution of coding PSNR. Bit-streams were produced with Test Model 5 [30] Fig.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The quantization error calculation block is described in [26]. 3) Calculate the ac energy of a decoded picture, , by using (29). 4) Predict the reference ac energy, , from the reconstructed ac energy of the reference pictures, , and , by using (30) and (31).…”

Section: Estimation Proceduresmentioning

confidence: 99%

“…AC energy of source and decoded sequences and NC-block ratio in each frame when encoded at 18 Mbps by TM5 ["Chromakey" (Sprinkling)"] (29). …”

mentioning

confidence: 99%

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Nonreference Method for Estimating PSNR of MPEG-2 Coded Video by Using DCT Coefficients and Picture Energy

Ichigaya¹,

Nakasu²

2008

IEEE Trans. Circuits Syst. Video Technol.

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Irreversible video coding like MPEG-2 may cause deterioration in picture quality depending on the properties of the source images and coding type. A monitoring technology to assess picture quality with fluctuations is needed for digital video services. We previously proposed a method to estimate the peak signal-tonoise-ratio (PSNR) of MPEG-2 coded video by using bit stream information. The method is categorized as a nonreference measurement that does not use source signals. It estimates the amount of distortion in each frame by analyzing the information in the decoded DCT coefficients. This paper describes a way to improve the accuracy of the PSNR estimation of our previous method, especially for the not coded blocks. The proposed method utilizes the temporal fluctuations of ac energy and estimates the PSNR of not-coded blocks from the inter-frame correlation of the decoded pictures. Experimental results show that the determination coefficient 2 between estimated and actual PSNRs is above 0.95 for every type of picture frame.Index Terms-AC energy, discrete cosine transform (DCT) information, MPEG-2, nonreference method, not-coded blocks, peak signal-to-noise ratio (PSNR).

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Estimation Proceduresmentioning

confidence: 99%

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Nonreference Method for Estimating PSNR of MPEG-2 Coded Video by Using DCT Coefficients and Picture Energy

Ichigaya¹,

Nakasu²

2008

IEEE Trans. Circuits Syst. Video Technol.

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“…JND depends on 3 factors : (1) display setting, e.g., viewing distance [9], monitor profiling, etc. ; (2) subjects; (3) image/video contents [10].…”

Section: Introductionmentioning

confidence: 99%

On The Benefit of Parameter-Driven Approaches for the Modeling and the Prediction of Satisfied User Ratio for Compressed Video

Zhu

Callet

Perrin³

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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The human eye cannot perceive small pixel changes in images or videos until a certain threshold of distortion. In the context of video compression, Just Noticeable Difference (JND) is the smallest distortion level from which the human eye can perceive the difference between reference video and the distorted/compressed one. Satisfied-User-Ratio (SUR) curve is the complementary cumulative distribution function of the individual JNDs of a viewer group. However, most of the previous works predict each point in SUR curve by using features both from source video and from compressed videos with assumption that the group-based JND annotations follow Gaussian distribution, which is neither practical nor accurate. In this work, we firstly compared various common functions for SUR curve modeling. Afterwards, we proposed a novel parameter-driven method to predict the video-wise SUR from video features. Besides, we compared the prediction results of source-only features based (SRC-based) models and source plus compressed videos features (SRC+PVS-based) models.

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“…JND depends on 3 factors: (1) display setting, e.g., viewing distance [17], monitor profiling, etc. ; (2) subjects; (3) image/video contents [11].…”

mentioning

confidence: 99%

Just noticeable difference (JND) and satisfied user ratio (SUR) prediction for compressed video

Zhu

Callet

2022

Proceedings of the 13th ACM Multimedia Systems Conference

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This paper contains the research proposal of Jingwen ZHU that was presented at the MMSys 2022 doctoral symposium. Just noticeable difference (JND) is the minimum amount of distortion from which human eyes can perceive difference between the original stimuli and distorted stimuli. With the rapid raise of multimedia demand, it is crucial to apply JND into the visual communication systems to use the least resources (e.g., bandwidth and storage) but without damaging the Quality of Experience (QoE) of end-users. In this thesis, we focus on the JND prediction for compressed video to guide the choice of optimal encoding parameters for video streaming service. In this paper, we analyse the limitations of the current JND prediction models and present five main research questions to address these challenges.

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A Statistical Analysis of MPEG-2 Picture Quality for Television Broadcasting

Cited by 17 publications

References 0 publications

Nonreference Method for Estimating PSNR of MPEG-2 Coded Video by Using DCT Coefficients and Picture Energy

Nonreference Method for Estimating PSNR of MPEG-2 Coded Video by Using DCT Coefficients and Picture Energy

On The Benefit of Parameter-Driven Approaches for the Modeling and the Prediction of Satisfied User Ratio for Compressed Video

Just noticeable difference (JND) and satisfied user ratio (SUR) prediction for compressed video

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