A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms

Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers...

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Vydáno v:	IEEE transactions on image processing Ročník 15; číslo 11; s. 3440 - 3451
Hlavní autoři:	Sheikh, H.R., Sabir, M.F., Bovik, A.C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.11.2006 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithm design and analysis Algorithms Applied sciences Assessments Data Interpretation, Statistical Detection, estimation, filtering, equalization, prediction Distortion Exact sciences and technology Human Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image quality Image quality assessment performance image quality study Information Storage and Retrieval - methods Information, signal and communications theory Judgments Laboratories Performance analysis Performance evaluation PSNR Quality assessment Quality Control Signal and communications theory Signal processing Signal, noise Software Software Validation Studies subjective quality assessment Telecommunications and information theory Testing Video compression Performance evaluation Image processing Algorithm Image quality assessment performance Subjective evaluation Video signal processing Image quality subjective quality assessment image quality study Algorithm performance Quality control Signal distortion Image evaluation
ISSN:	1057-7149, 1941-0042
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Abstract	Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25 000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community . This would allow other researchers to easily report comparative results in the future
AbstractList	Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25,000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community. This would allow other researchers to easily report comparative results in the future.Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25,000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community. This would allow other researchers to easily report comparative results in the future. Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25 000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community . This would allow other researchers to easily report comparative results in the future To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. [...] we have made the data from the study freely available to the research community . Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25,000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community. This would allow other researchers to easily report comparative results in the future.
Author	Bovik, A.C. Sabir, M.F. Sheikh, H.R.
Author_xml	– sequence: 1 givenname: H.R. surname: Sheikh fullname: Sheikh, H.R. organization: Dept. of Electr. & Comput. Eng., Texas Univ – sequence: 2 givenname: M.F. surname: Sabir fullname: Sabir, M.F. – sequence: 3 givenname: A.C. surname: Bovik fullname: Bovik, A.C.
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18214579$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/17076403$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1109/TIP.2005.859378 10.1109/ACSSC.2003.1292216 10.1016/j.imavis.2004.03.002 10.1109/26.718563 10.1109/83.841940 10.1117/12.220346 10.1109/26.477498 10.1117/1.1455011 10.1117/12.201231 10.1109/TIP.2005.859389 10.1117/12.537129 10.1117/12.320101 10.1109/ICIP.1998.727226
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References_xml	– start-page: 207 year: 1995 ident: ref8 publication-title: Visual Models for Target Detection and Recognition – year: 2005 ident: ref1 publication-title: LIVE Image Quality Assessment Database Release 2 – ident: ref26 doi: 10.1109/TIP.2005.859378 – year: 2003 ident: ref24 article-title: multi-scale structural similarity for image quality assessment publication-title: IEEE Asilomar Conf Signals Systems and Computers doi: 10.1109/ACSSC.2003.1292216 – year: 2000 ident: ref11 publication-title: Handbook of Parametric and Nonparametric Statistical Procedures – ident: ref18 doi: 10.1016/j.imavis.2004.03.002 – ident: ref21 doi: 10.1109/26.718563 – year: 1999 ident: ref27 publication-title: Applied Statistics and Probability for Engineers – volume: xxiv start-page: 946 year: 1993 ident: ref20 article-title: dctune: a technique for visual optimization of dct quantization matrices for individual images publication-title: Soc Inf Display Dig Tech Papers – ident: ref23 doi: 10.1109/83.841940 – year: 2001 ident: ref13 publication-title: JPEG2000 Image Compression Fundamentals Standards and Practice – year: 2004 ident: ref28 publication-title: Electronic Statistics Textbook WEB – ident: ref4 doi: 10.1117/12.220346 – year: 2000 ident: ref2 publication-title: Final report from the video quality experts group on the validation of objective models of video quality assessment – year: 2003 ident: ref19 publication-title: JNDmetrix Technology – year: 0 ident: ref17 – start-page: 179 year: 1993 ident: ref7 publication-title: Digital Images and Human Vision – year: 0 ident: ref12 publication-title: VQEG The Video Quality Experts Group – year: 2003 ident: ref15 publication-title: Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment Phase II – ident: ref3 doi: 10.1109/26.477498 – volume: 11 start-page: 206 year: 2002 ident: ref5 article-title: statistical evaluation of image quality measures publication-title: J Electron Imag doi: 10.1117/1.1455011 – ident: ref16 doi: 10.1117/12.201231 – ident: ref25 doi: 10.1109/TIP.2005.859389 – ident: ref10 doi: 10.1117/12.537129 – ident: ref6 doi: 10.1117/12.320101 – year: 0 ident: ref14 publication-title: Methodology for the Subjective Assessment of the Quality of Television Pictures – year: 0 ident: ref22 publication-title: CIPIC PQS ver 1 0 – ident: ref9 doi: 10.1109/ICIP.1998.727226
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Snippet	Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA)... To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest...
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SubjectTerms	Algorithm design and analysis Algorithms Applied sciences Assessments Data Interpretation, Statistical Detection, estimation, filtering, equalization, prediction Distortion Exact sciences and technology Human Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image quality Image quality assessment performance image quality study Information Storage and Retrieval - methods Information, signal and communications theory Judgments Laboratories Performance analysis Performance evaluation PSNR Quality assessment Quality Control Signal and communications theory Signal processing Signal, noise Software Software Validation Studies subjective quality assessment Telecommunications and information theory Testing Video compression
Title	A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
URI	https://ieeexplore.ieee.org/document/1709988 https://www.ncbi.nlm.nih.gov/pubmed/17076403 https://www.proquest.com/docview/867338672 https://www.proquest.com/docview/69021447 https://www.proquest.com/docview/889409314
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