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Computational Perception and Image Quality Lab
Data compression, image analysis, human visual system models, and natural scenes
318 Advanced Technology Research Center
School of Electrical and Computer Engineering
Oklahoma State University
Stillwater, OK 74078 USA
Phone: (405) 744-2192

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About the Computational Perception and Image Quality Lab

Research in the Computational Perception and Image Quality Lab broadly concerns coding and analysis of visual information.

We study topics in image processing, including image modeling, analysis, and compression, as well as topics in visual coding and perception, including natural scene statistics and visual psychophysics.


We are currently researching projects in:
 
Full-Reference and No-Reference Image and Video Quality Assessment: CSIQ, VSNR, MAD, S3, FISH, DESIQUE, NJQA, and many more...
ViS3 An Algorithm for Video Quality Assessment via Analysis of Spatial and Spatiotemporal Slices
CAREER: Content-Based Image and Video Coding Using Higher-Level Models of Human Vision (preliminary results on TS-3AFC, subband distortion allocation, perceptual edges)
S3 Measure of Local Perceived Sharpness in Natural Images
CSIQ: Categorical Image Quality Database
MAD: The Most Apparent Distortion: A Dual-Strategy for Image Quality Assessment

Main Subject Detection via Adaptive Feature Refinement

Visually Optimal Compression of Medical Imagery
New models of visual masking based on classification
Estimating the information content in natural scenes
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio
Visual Detection and Summation in Natural Scenes
DCQ: Dynamic Contrast-Based Quantization for JPEG-2000
Response Normalization in Retinal Ganglion Cells
 
More details on our projects are available from our projects' page.

More details on our research are available from our publications.
 
 
 
 
 
 

Lab Announcements

Yi Zhang's work "No-reference image quality assessment based on log-derivative statistics of natural scenes" has been published in the Journal of Electronic Imaging, December 2013.
Damon Chandler's 53-page review paper on image quality assessment was published in ISRN Signal Processing in Feb. 2013.
Phong Vu's work on video quality assessment was presented at VPQM 2013.
Yi Zhang, Mushfiqual Alam, Phong Vu, and Punit Singh each presented their respective papers at SPIE Electronic Imaging 2013.
Damon Chandler received an NSF CAREER award on Content-Based Image and Video Coding Using Higher-Level Models of Human Vision in February 2011.
   
 
 
 

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Collaborators

Visual Communications Lab (Cornell; S. S. Hemami)
Field Lab (Cornell; D. J. Field)
CEASAR Lab (OSU; S. Sohoni)
Dan Graham (Dartmouth Math)
C++Builder Developer's Journal
 
 
 
 
 
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