Blind image quality assessment through anisotropy.

By Salvador Gabarda, Gabriel Cristóbal. Journal of The Optical Society of America A-optics Image Science and Vision, 2007

324 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/e39a6d8a1729c49f5d1e141b0334730c38e7c867

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. A. Bovik
2. Anish Mittal
3. Lei Zhang
4. Damon M. Chandler
5. Michele A. Saad
6. Ke Gu
7. Zhou Wang
8. Weisi Lin

In Media Technology

1. A. Bovik
2. Anish Mittal
3. Lei Zhang
4. Damon M. Chandler
5. Zhou Wang
6. Michele A. Saad
7. Ke Gu
8. Anush K. Moorthy

Citations in the corpus

• A DCT Statistics-Based Blind Image Quality Index (2010, methodology): “In this work, we compute a modified version of the anisotropy measure described in [ 11 ].”
• RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012, unknown): “Training-based NR QA techniques have resulted in algorithms that perform at least as well as MSE, which has the benefit of a reference image [9], [10].”
• No-Reference Image Quality Assessment in the Spatial Domain (2012, methodology): “Li devised a set of heuristic measures to characterize visual quality in terms of edge sharpness, random noise and structural noise [18] while Gabarda and Cristobal, modeled anisotropies in images using Renyi entropy [19].”
• Blind Image Quality Assessment Using a General Regression Neural Network (2011, methodology): “Following this, the authors of [ 62 ] deploy the entropy to measure the anisotropy of images, and use this statistic for blind IQA.”
• Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality (2011, methodology): “Gabrada and Cristobal proposed an innovative strategy for blind IQA which utilized the Renyi entropy measure [30] along various orientations to measure anisotropy.”
• Blind/Referenceless Image Spatial Quality Evaluator (2011, methodology): “BRISQUE may be easily extended beyond the distortions considered here making it suitable for a general-purpose blind IQA problems.”
• Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content (2010, unknown): “One example is the metric based upon the image anisotropy [14] proposed by Gabarda and Cristóbal [15].”
• Seven Challenges in Image Quality Assessment: Past, Present, and Future Research (2013, background): “In [264, 265], Gabarda and Cristóbal presented an entropy-based NR IQA algorithm.”