Statistical evaluation of image quality measures

By Ismail Avcibas, Bülent Sankur, Khalid Sayood. J. Electronic Imaging, 2002

754 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fc52c657a46973ae5e6f558e004389c170cee377

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In Computer Vision and Pattern Recognition

1. A. Bovik
2. Zhou Wang
3. Ming-Hsuan Yang
4. Dacheng Tao
5. Anush K. Moorthy
6. Lei Zhang
7. Kede Ma
8. Chen Change Loy

Citations in the corpus

• Quality-aware images (2006, neutral): “Implementation”
• Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays (2021, methodology): “For the image regeneration task, we use Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM) [1] to measure the quality of regenerated images.”
• Image information and visual quality (2006, background): “In [15] and [ 16 ], a number of these were evaluated for the purpose of quality assessment.”
• Steganalysis of LSB matching using differences between nonadjacent pixels (2016, background): “[2] proposed a framework for learning based steganalysis (see Fig.”
• Modern Image Quality Assessment (2006, methodology): “The KLD has also been employed to quantify the distributions of image pixel intensity values for the evaluation of compressed image quality [109, 110].”
• Image Quality Metrics: PSNR vs. SSIM (2010, methodology): “Other studies have shown that the MSE, and consequently the PSNR, have the best performance in assessing the quality of noisy images [2].”
• A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms (2006, methodology): “In [5], only 50 JPEG compressed images derived from a face data-base were used.”
• An information fidelity criterion for image quality assessment using natural scene statistics (2005, background): “In [14] and [15], a number of these are evaluated for the purpose of QA.”
• Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum (2003, neutral): “software [199, 283, 336, 338, 391, 423, 636, 455, 520, 557, 560, 581, 834, 709, 805, 904, 905, 1110, 1253, 1285, 1355, 1378, 1384, 1449, 1464, 1500, 1509, 1530, 1668, 1672, 1722, 1732, 1795, 1794, 2087, 1833, 1874, 1897, 2032, 2033, 2031] solder joint(s) [840, 1499, 2051] sonar [14, 245, 739, 860, 1”
• Current status and key issues in image steganography: A survey (2014, background): “[99] I.”
• Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition (2014, methodology): “…and perspectives proposed in the literature for IQA, the initial feature selection process to determine the set of 25 IQMs has been carried out according to four general criteria, which intend that the final method complies to the highest possible extent with the desirable requirements set for…”