Quality-aware images

By Zhou Wang, Guixing Wu, Hamid R. Sheikh, Eero P. Simoncelli, E. Yang, A. Bovik. 2006

329 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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. Dacheng Tao
3. Kede Ma
4. Zhou Wang
5. Tomer Michaeli
6. Yochai Blau
7. Lei Zhang
8. Radu Timofte

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, background): “In [123], the authors introduced the concept of quality-aware image, in which the RR information is encoded as invisible hidden messages.”
• dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs (2017, background): “They can be categorized into full-reference models (FR, where the reference image is fully available when evaluating a distorted image) [3], reduced-reference models (RR, where only partial information about the reference image is available) [4], and blind/no-reference models (NR, where the referenc”
• Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality (2011, methodology): “In the past, such NSS-based QA algorithms have been successfully deployed for FR IQA [5], [6], for RR IQA [3], [4], and to a small extent, for NR IQA [11].”
• 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.”
• RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012, background): “performs better than some of the best FR algorithms, whereas is competitive with PSNR and outperforms [12].”
• Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, unknown): “The perceptual quality of an image is often defined as the divergence between the distribution of the reconstruction and the statistics of natural images [62].”
• Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation (2009, methodology): “Three different but related types of approaches have been employed in existing RRIQA methods [9]–[16].”