PSNR estimation scheme using coefficient distribution of frequency domain in H.264 decoder

Shim, Seong-Joon; Moon, Joo-Hee; Han, Jong-Ki

doi:10.1049/el:20082512

Cited by 22 publications

(15 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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%

See 1 more Smart Citation

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

Hemami

Reibman

2010

Signal Processing: Image Communication

122

View full text Add to dashboard Cite

Section: Direct Estimation Of Mean-squared Errormentioning

confidence: 99%

“…The basic approach is to model the DCT coefficients using a Laplacian distribution, and estimate the Laplacian parameter for each of the 8 Â 8 coefficients. However, this has been extended to generalized Gaussian [90] and Cauchy distributions [91,92] as well.…”

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

View full text Add to dashboard Cite

“…Contrary to the assumption of Laplacian distribution to model DCT coefficients, it has been argued in [171] that a Cauchy distribution better suits the H.264/AVC encoded data in the process of quality estimation. The proposed approach has been found to be better than the Laplacian distribution [165] in terms of bias between the actual and estimated values of PSNR.…”

Section: Bitstream Layer Modelmentioning

confidence: 99%

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

et al. 2014

View full text Add to dashboard Cite

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...

show abstract

“…Methods have been proposed to estimate the perceived quality of compressed images by identifying and measuring the compression artifacts [1][2][3][4][5][6]. There are also research projects done on estimating the objective quality, in terms of mean squared error (MSE) or peak signal-to-noise ratio (PSNR), without referring to the original pictures [7][8][9][10][11]. Postprocessing techniques have also been explored, ranging from various forms of adaptive filtering of image content [12][13][14][15][16][17][18][19] to optimized decoding of compressed images [20][21][22][23][24].…”

Section: Motivation and Objectivementioning

confidence: 99%

“…Conventionally, the quality of a distorted signal is measured by its difference from the original: the larger the error, the lower the quality. A common measure used is the mean squared error (MSE): 11) where M and N are the total number of columns and rows of the picture;…”

Section: Image and Video Qualitymentioning

confidence: 99%

No-reference objective quality assessment and enhancement for compressed images and videos

Gao¹

View full text Add to dashboard Cite

I would like to express my deepest appreciation and sincere gratitude to my supervisor, Associate Professor Tan Yap Peng, for his invaluable inspiration, guidance and support. This work would not have been possible without him. My thanks also go to Dr. Wang Ci and Dr. Chen Zhenzhong, for their help and support. I would like to acknowledge School of EEE, NTU, for awarding me the research scholarship. Its excellent facilities and resources have also been essential to this work. Finally, I would like to thank my parents for their love, understanding, and support through my years of study away from home.

show abstract

PSNR estimation scheme using coefficient distribution of frequency domain in H.264 decoder

Cited by 22 publications

References 3 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

No-reference objective quality assessment and enhancement for compressed images and videos

Contact Info

Product

Resources

About