Reduced-reference image quality assessment using a wavelet-domain natural image statistic model

By Zhou Wang, Eero P. Simoncelli. 2005

504 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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. A. Bovik
3. Luc Van Gool
4. Lei Zhang
5. Chen Change Loy
6. Ming-Hsuan Yang
7. Radu Timofte
8. Kede Ma

In Media Technology

1. A. Bovik
2. Dacheng Tao
3. Kede Ma
4. Zhou Wang
5. Tomer Michaeli
6. Yochai Blau
7. Damon M. Chandler
8. Xiaojun Wu

Citations in the corpus

• The Perception-Distortion Tradeoff (2017, methodology): “For example, [41, 43, 21] proposed a perceptual quality index based on the Kullback-Leibler (KL) divergence between the distribution of the wavelet coefficients of x̂ and that of natural scenes.”
• From QoS to QoE: A Tutorial on Video Quality Assessment (2015, methodology): “In this section, we introduce WNISM, recognized as the standard NSS based RR model proposed by [122].”
• dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs (2017, background): “Therefore, statistics of raw [4], [6], [46], [47] and normalized [48], [49] wavelet coefficients, and wavelet coefficient correlations in the neighborhood [10], [29], [50]–[52] can be individually or jointly modeled as image naturalness measurements.”
• Blind Image Quality Assessment Using a General Regression Neural Network (2011, background): “In RR algorithms [17]–[ 19 ], the reference image is not available, yet some features extracted from the reference image are made available.”
• GIQA: Generated Image Quality Assessment (2020, background): “RR-IQA [37] aims to evaluate the perceptual quality of a distorted image through partial information of the corresponding reference image.”
• Seven Challenges in Image Quality Assessment: Past, Present, and Future Research (2013, methodology): “For example, in [279], Wang and Simoncelli presented an RR IQA method which operates based on a wavelet-domain statistical model of images.”
• Blind Prediction of Natural Video Quality (2014, background): “RR-I/VQA refers to I/VQA models that require partial information about the reference signal in order to predict the quality of a test signal.”