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

Ichigaya, Atsuro; Nakasu, Eisuke

doi:10.1109/tcsvt.2008.920658

Cited by 26 publications

(9 citation statements)

References 25 publications

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“…The first methods in this class of NR QE were designed to predict the Mean-Squared-Error (MSE) caused by block-based compression like MPEG-2 [83][84][85][86][87], JPEG [87,88], or H.264 [89,90,87,91,92]. With the exception of [84], which uses the decoded pixels v test , these techniques use information only from the received bitstream.…”

Section: Direct Estimation Of Mean-squared Errormentioning

confidence: 99%

No-reference image and video quality estimation: Applications and human-motivated design

Hemami

Reibman

2010

Signal Processing: Image Communication

122

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Section: Direct Estimation Of Mean-squared Errormentioning

confidence: 99%

No-reference image and video quality estimation: Applications and human-motivated design

Hemami

Reibman

2010

Signal Processing: Image Communication

122

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“…One such example is found in [191] where an estimate of PSNR has been computed for MPEG-2 coded videos using DCT coefficients. This is actually an improved version of the authors' earlier contribution [192] in which they modeled the distribution of DCT coefficients as a Laplacian pdf to calculate PSNR of the video frames one-by-one for all types, i.e., I, P, and B frames.…”

Section: Statistics Of Transform Coefficientsmentioning

confidence: 99%

“…Therefore, the authors conjectured that this happens as a result of fall in the amount of DCT coefficients information which is available for B frames due to processes of rate control and motion compensation. Henceforth, a hybrid approach to resolve this issue http://jivp.eurasipjournals.com/content/2014/1/40 has been found in [191] where picture energy has also been used in addition to DCT coefficients. There is a significant improvement of correlation with estimated and actual PSNR, in the case when the proposed method was tested on SDTV and HDTV sequences.…”

Section: Statistics Of Transform Coefficientsmentioning

confidence: 99%

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

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The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods. Keywords:No-reference; Image quality assessment; Video quality assessment; Perceptual quality 1 Review IntroductionThere has been a tremendous progress recently in the usage of digital images and videos for an increasing number of applications. Multimedia services that have gained wide interest include digital television broadcasts, video streaming applications, and real-time audio and video services over the Internet. The global mobile data traffic grew by 81% in 2013, and during 2014, the number of mobileconnected devices will exceed the number of people on earth, according to predictions made by Cisco. The video portion of the mobile data traffic was 53% in 2013 and is expected to exceed 67% by 2018 [1]. With this huge increase in the exposure of image and video to the human eye, the interest in delivering quality of experience (QoE) may increase naturally. The quality of visual media can get degraded during capturing, compression, transmission, reproduction, and displaying due to the distortions that might occur at any of these stages. The legitimate judges of visual quality are humans as end users, the opinions of whom can be obtained by subjective experiments. Subjective experiments involve a panel of participants which are usually non-experts, also referred to as test subjects, to assess the perceptual quality of given test material such as a sequence of images or videos. Subjective experiments are typically conducted in a controlled laboratory environment. Careful planning and several factors including assessment method, selection of test material, viewing conditions, grading scale, and timing of presentation have to be considered prior to a subjective experiment. For example, Recommendation (ITU-R) BT.500 [2] provides detailed guidelines for conducting subjective experiments for the assessment of quality of television pictures. The outcomes of a subjectiv...

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“…Using (8), and the values for a provided in Table 7, the best values for v 4 and v 5 were re-calculated, for all the clips in the four display formats, and are presented in Table 8. The v 4 and v 5 values, and the MSE errors, are very similar to the values presented in Table 6 (only for SD display format), verifying the accuracy of the model, and the independence of the a values from the other parameters.…”

Section: Existing Video Quality Modelsmentioning

confidence: 99%

“…Several comparisons and characterizations of different codecs have been made, contrasting the image quality obtained regarding to the original image (FR models), or predicting the image quality based only in the degraded image (NR) [4][5][6][7][8][9]. However, most of these works have been made using non-perceptual metrics such as PSNR.…”

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confidence: 99%

A parametric model for perceptual video quality estimation

Joskowicz

Ardao

2010

Telecommun Syst

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In this paper, a parametric model is proposed which provides estimation for the perceived quality of video, coded with different codecs, at any bit rate and display format. The video quality metric used is one of the standardized Full Reference models in Recommendations ITU-T J.144 and ITU-R BT.1683. The proposed model is based on the video quality estimation described in Recommendation ITU-T G.1070, but incorporates different enhancements, allowing a much better estimation of the perceptual MOS values, especially in low bit rate ranges. The error obtained with the proposed model, regarding to the ITU models, is between the ITU algorithms error margins, according to the subjective tests developed by the VQEG. Studies were made for more than 1500 processed video clips, coded in MPEG-2 and H.264/AVC, in bit rate ranges from 50 kb/s to 12 Mb/s, in SD, VGA, CIF and QCIF display formats.

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

Cited by 26 publications

References 25 publications

No-reference image and video quality estimation: Applications and human-motivated design

No-reference image and video quality estimation: Applications and human-motivated design

No-reference image and video quality assessment: a classification and review of recent approaches

A parametric model for perceptual video quality estimation

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