“The formatting of images can be considered as an optimization problem, whose cost function is a quality assessment algorithm. There is a trade-off between bit budget per pixel and quality. To maximize the quality and minimize the bit budget, we need to measure the perceived quality.
In this thesis, we focus on understanding perceived quality through visual representations that are based on visual system characteristics and color perception mechanisms. Specifically, we use the contrast sensitivity mechanisms in retinal ganglion cells and the suppression mechanisms in cortical neurons. We utilize color difference equations and color name distances to mimic pixel-wise color perception and a bio-inspired model to formulate center surround effects. We combine our findings from visual system and color perception with data-driven methods to generate visual representations and measure their quality.
The majority of existing data-driven methods require subjective scores or degraded images. In contrast, we introduce an unsupervised approach that only utilizes generic images. In addition to introducing quality estimators, we analyze the role of spatial pooling and boosting in image quality assessment. “
– D.Temel, Oct, 2016
Prof. Ghassan AlRegib [Advisor], School of Electrical and Computer Eng., Georgia Institute of Technology
Prof. James H. McClellan, School of Electrical and Computer Eng., Georgia Institute of Technology
Prof. David V. Anderson, School of Electrical and Computer Eng., Georgia Institute of Technology
Prof. Antony J. Yezzi, School of Electrical and Computer Eng., Georgia Institute of Technology
Prof. Nagi Gabreal, H. Milton Stewart School of Indust. and Syst. Eng., Georgia Institute of Technology