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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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