Foveated video quality assessment
By Sanghoon Lee, Marios S. Pattichis, A. Bovik. 2002
188 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
Citations in the corpus
- What do we need to build explainable AI systems for the medical domain? (2017, background): “In the bottom two rows is an asymptomatic example. int = intensity ear coordinate systems has led to the earlier development of very low bitrate video coding as demonstrated in (Lee et al., 2001, 2002).”
- 41 OBJECTIVE VIDEO QUALITY ASSESSMENT (2003, background): “Most image quality assessment models work with foveal vision; a few incorporate peripheral vision as well [17-20].”
- Fully Deep Blind Image Quality Predictor (2017, background): “Only a local area around the fovea can be perceived with high resolution because of foveation [4]–[6].”
- Modern Image Quality Assessment (2006, unknown): “, [58–60]) may also be employed.”
- From QoS to QoE: A Tutorial on Video Quality Assessment (2015, background): “Different from existing quality metrics based on static foveated vision [99]–[101], AFViQ simulates the dynamic foveation by”
- The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics (2008, background): “Only few metrics attempt to model the focus of attention and consider it for computing the overall video quality, for example through the process of “foveation” [40], or using object/face segmentation techniques [41].”
- Foveated video compression with optimal rate control (2001, methodology): “remote training systems, and remote surgery) [6]–[10].”
- Digital Video Quality: Vision Models and Metrics (2005, background): “In fact, this representation may be the result of the properties of natural spectra (Lee et al., 2002).”