Image Information and Visual Quality
Hamid Rahim Sheikh and Alan C. Bovik
IEEE Transactions on Image Processing, Feb. 2006
Presented by Xiaoli Wang for ECE 776 Project
Introduction
Quality Assessment (QA) research for image processing
“Full-reference” QA methodsInterpret image quality as fidelity with a “reference” image
Attempt to achieve consistency with the human visual system (HVS)
Propose a visual information fidelity measurement for image QAQuantify the loss of image information to the distortion process and explore the relationship between image information and visual quality
Natural image source
Channel (Distortion)
HVS
HVS E
FDC
Visual Information FidelityDistortion Model
D denotes the random field (RF) from a subband in the reference signalG is a deterministic scalar gain fieldC stands for the RF from a subband in the reference signalV represents a stationary additive white Gaussian noise RF
This model captures important and complementary distortion types: blur, additive noise, and global or local contrast changes
Human Visual System Model
Approaching the HVS as a “distortion channel” that imposes limits on how much information could flow through it
Lumping all sources of HVS uncertainty into an AWGN component
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Visual Information Fidelity (cont’)Visual Information Fidelity Criterion (IFC)
Mutual information for the reference / test images
Represent the information that could ideally be extracted by the brain from a particular subband in the reference and the test images
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Visual Information Fidelity (cont’)Visual Information Fidelity Criterion (IFC) (cont’)
A simple ratio of the two information measurements relates very well with visual quality
Properties of VIFVIF is bounded below by zero
VIF is exactly unity if calculated between the reference image and its copy
A linear contrast enhancement of the reference image will result in a VIF value larger than unity, signifying a superior visual quality
Similarities of VIF with HVS-based methodsThe numerator is basically IFC (information fidelity criterion) and, hence, is functionally similar to HVS-based methods
The denominator can be thought of as a content dependent adjustment
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VIF( ; | )
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Image samples
Reference image, VIF=1.0
Contrast enhanced, VIF=1.10
Blurred, VIF=0.07
JPEG compressed, VIF=0.10
ExperimentsDMOS vs. four objective quality criteria
Distortion typesJPEG2000 (red)
JPEG (green)
White noise in RGB space (blue)
Gaussian blur (black)
Transmission errors in JPEG 2000 stream over fast-fading Rayleigh channel (cyan)
Thanks
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