Debarati Kundu (The University of Texas at Austin), “Subjective and Objective Quality Evaluation of Synthetic and High Dynamic Range Images” (2016)

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

10 years of news and resources for members of the IEEE Signal Processing Society

Debarati Kundu (The University of Texas at Austin), “Subjective and Objective Quality Evaluation of Synthetic and High Dynamic Range Images” (2016)

Debarati Kundu (The University of Texas at Austin), “Subjective and Objective Quality Evaluation of Synthetic and High Dynamic Range Images” (2016) Advisor: < a href= “http://users.ece.utexas.edu/~bevans/”> Evans, Brian L.

Measuring digital picture quality, as perceived by human observers, is increasingly important in applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images are ubiquitous and provide 8 bits per color per pixel. High dynamic range (HDR) images, which can be captured by smart phones and digital cameras, provide an enhanced range of luminance and chrominance values by using 16 or 32 bits per color per pixel.

For synthetic SDR and natural HDR images, the authors design and release public databases, conduct subjective visual quality experiments, evaluate objective quality measures against the subjective quality measures, and propose no-reference objective measures. They evaluate 50+ full-, reduced- and no-reference objective measures. For the HDR database, they conduct a large-scale crowdsourced study to gather 300,000+ opinion scores on 1,800+ images from 5,000+ unique observers.

For the mean subtracted contrast normalized, standard deviation, and gradient images for synthetic SDR and natural HDR images, they show that the amplitude statistics are characterized by generalized Gaussian distributions, and that visual distortions show up as deviations from these scene statistics. Among no-reference measures, those based on scene statistics have the highest correlations with human visual quality scores for synthetic SDR and natural HDR images, just as they do in many studies involving natural SDR images.

SPS on Twitter

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar