A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images

By Shiqi Wang, Kede Ma, Hojatollah Yeganeh, Zhou Wang, Weisi Lin. IEEE Signal Processing Letters, 2015

377 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/ce4f522eab5992d0744305e6cf215ac928b1636f

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. Dacheng Tao
2. Lei Zhang
3. Luc Van Gool
4. Xiaokang Yang
5. Ming-Hsuan Yang
6. Chen Change Loy
7. Kede Ma
8. Ling Shao

In Media Technology

1. Ke Gu
2. Weisi Lin
3. Guangtao Zhai
4. Xiaokang Yang
5. Kede Ma
6. Dacheng Tao
7. Lei Zhang
8. Chongyi Li

Citations in the corpus

• An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “Other available FIQA metrics include the patch-based contrast quality index (PCQI) [302], the colorfulness-based PCQI (CPCQI) [303], the Gradient Magnitude Similarity Deviation (GMSD) [304], the visual information fidelity (VIF) [305], the visual saliency index (VSI) [306], the tone-mapped image…”
• Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (2018, methodology): “proposed PCQI metric was proven to highly correlate with subjective quality scores on enhancement-relevant databases [35], but it does not take the influence of colorfulness into consideration, which is obviously an important index of image quality.”
• A deep CNN method for underwater image enhancement (2017, methodology): “As described in [7], we used Entropy, and patch-based contrast quality index(PCQI) [26] to evaluated the performance .”
• Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior (2016, positive): “Table IV summarizes the average MSE, PSNR, entropy, PCQI, UCIQE, and PT results on the 500 tested underwater images with a size of 720×1280.”
• Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors (2022, positive): “For the 300 underwater images without the corresponding ground truth images, we employ the four non-reference evaluation metrics UIQM [41], UCIQE [42], PCQI [43], and Entropy [39] to measure the performance of different methods.”
• No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization (2017, methodology): “Lately, the IQA of contrast distortion has attracted broad attentions [16-20], since this study can be used for image”
• Reversible data hiding: Advances in the past two decades (2016, methodology): “map is produced so that the patch-based contrast quality index (PCQI) method can provide accurate predictions on the human perception of contrast variations [148].”
• Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach (2017, background): “signal processing approach, SPD has been found to be useful in image quality assessment of contrast-changed [66] and stereoscopic images [67].”
• Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light (2019, background): “THE “CRITERION” COLUMN LISTS THE METRICES OF MEAN SQUARED ERROR (MSE), ROOT MEAN SQUARD ERROR (RMSE), PEAK SIGNAL TO NOISE RATIO (PSNR), STRUCTURAL SIMILARITY INDEX (SSIM), CONTRAST-TO-NOISE RATIO (CNR), PATCH-BASED CONTRAST QUALITY INDEX (PCQI) [21], UCIQE, UIQM, AND BLIND/ REFERENCELESS IMAGE SPAT”