Digital Video Image Quality and Perceptual Coding

By Chun-Hsien Chou, Kuo-Cheng Liu, Chien-Sheng Lin. J. Electronic Imaging, 2005

442 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/cbcaef435d52b16dc5655ef87a98ee3b1ebab833

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. Kede Ma
3. Zhou Wang
4. Qian Zhang
5. Tongliang Liu
6. Wentao Liu
7. Kaishun Wu
8. Yanjiao Chen

Citations in the corpus

• dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs (2017, background): “2708503 decade, with a large variety of IQA models proposed [1], [2].”
• Dynamic range independent image quality assessment (2008, result): “Similar responses are also observed in LDR-HDR and HDR-LDR cases.”
• Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison (2011, background): “Further, fundamentals of perceptual models for video quality metrics and overviews of metrics developed prior to 2005 are provided in [39], [40].”
• From QoS to QoE: A Tutorial on Video Quality Assessment (2015, methodology): “For example, [32], [69] mainly follow the psychophysical/engineering approach classification method; [31], [70]–[72] mainly adopt the reference-based clas-sification method, and [47] adopts a combination of the two.”