Image Quality Assessment Based on Gradient Similarity

By Anmin Liu, Weisi Lin, Manish Narwaria. IEEE Transactions on Image Processing, 2012

718 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/2b819bef80c02d5d4cb56f27b202535e119df988

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. Xiaokang Yang
2. Weisi Lin
3. Ke Gu
4. Guangtao Zhai
5. Luc Van Gool
6. Chen Change Loy
7. Kede Ma
8. Dacheng Tao

In Media Technology

1. Weisi Lin
2. Ke Gu
3. Guangtao Zhai
4. Xiaokang Yang
5. Kede Ma
6. Jinjin Gu
7. Chao Dong
8. Xiongkuo Min

Citations in the corpus

• A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images (2015, methodology): “The PCQI map produced by the proposed method also indicates local quality variations, which are useful in guiding the future design of advanced image enhancement algorithms.”
• Blind Image Quality Estimation via Distortion Aggravation (2018, methodology): “GSI [24], FSIM [25], GMSD [26], PSIM [14].”
• The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement (2016, methodology): “, gradient and phase) in the IQA exploration, feature similarity index (FSIM) [11], gradient similarity index (GSIM) [12], internal generative”
• Using Free Energy Principle For Blind Image Quality Assessment (2015, background): “On the other hand, image gradient computation, a very classical topic in image processing, is also valid in IQA performance gains [11], [12].”
• Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition (2014, methodology): “It has been tested on publicly available attack databases of iris, fingerprint and 2D face, where it has reached results fully comparable to those obtained on the same databases and following the same experimental protocols by more complex trait-specific top-ranked approaches from the state-of-the-a”
• PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration (2020, methodology): “ows the scatter plots of MOS score vs. IQA methods for all SR algorithms. The second row gives scatter plots for GAN-based SR algorithms MS-SSIM [50], IFC [41], VIF [40], VSNR-FR [7], RFSIM [57], GSM [30], SRSIM [55], FSIM and FSIM C[58], SFF [8], VSI [56], SCQI [2], LPIPS-Alex and -VGG [60], PieAPP”
• A Haar wavelet-based perceptual similarity index for image quality assessment (2016, background): “Changes in contrast and structure are captured by considering local gradients in [11], while the squared difference in pixel values between the reference image and the distorted image is used to measure luminance variations.”
• Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, neutral): “Meanwhile, the segmentation model is often requested to be deployed to some hydrological stations without powerful computation resources.”