dynamic range independent image quality...

Dynamic Range Independent

Image Quality Assessment

Tunç Aydin*, Rafał Mantiuk,

Karol Myszkowski and Hans-Peter Seidel

MPI Informatik

Image Quality Assessment

Example Applications

Global Illumination

Speed up rendering

without affecting

quality

[Myszkowski 2002]

Benchmarking

systems and

algorithms

[Dabov 2008]

Image Processing Image Compression

How much

compression

without visible

artifacts?

Subjective Experiments

Quality of the

distorted

image ?

Rate

the

Quality

+ Reliable - High cost

Simple Quality Metrics

MSE ~ 280 MSE ~ 280 MSE ~ 280 !

Based on the Differences between Images

Examples: Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR)

Random Noise Blur ~15% Decreased

Luminance

Reference

Human Visual System (HVS)

Based Metrics

Based on the Visible Differences between Images Examples: Visible Differences Predictor (VDP) [Daly 93], HDR-VDP [Mantiuk et al. 05],

Visual Discrimation Model (VDM) [Lubin 95]

Probability of Detection:

~15% Decreased

Luminance

Random Noise Distortion

Map

Distortion

Map

From Distortion Magnitude to

Structural Similarity

Measure the Preservance of Image Structure

Examples: Structural Similarity Index Metric (SSIM) [Wang et al. 04]

Reference Contrast

Enhancement Distortions may be

Visible

but

Not

objectionable

Visibility Structure

Simple metrics No No

HVS based

metrics

Limited or full

dynamic range No

Structural

similarity

based metrics

Calibration

challenging due

to “abstract”

parameters

Yes

Our approach: Hybrid of HVS and

Structural Similarity

Focus Point: Image Pair with

Different Dynamic Ranges

Similar

appearance …

… yet very

different

luminance

Outline

Detecting visibility thresholds

Full Dynamic Range Human Visual System

(HVS) Model

Detecting structural changes

A set of new distortion measures

Advantages over previous work

Possible applications

Human Visual System

(HVS) Model

…of the entire visible dynamic range

Human Visual System Model

Luminance

Masking

Contrast

Sensitivity

Light

Scattering

[ JND ]

[ LUMINANCE ]

Channel

Decomposition

Luminance

Masking Contrast

Sensitivity

Light

Scattering Channel

Decomposition

Decreased sensitivity due to glare around bright spots

[Deeley et al. 1991]

Luminance

Masking Contrast

Sensitivity

Light

Scattering

Decreased sensitivity due to glare around bright spots

[Deeley et al. 1991]

Channel

Decomposition

Luminance

Masking Contrast

Sensitivity

Channel

Decomposition

Light

Scattering

Log

Luminance

# of

JNDs

Transform image luminance to Just Noticeable Difference (JND) Space

[Mantiuk et al. 2005]

Luminance

Masking Contrast

Sensitivity

Light

Scattering Channel

Decomposition

Low

Sensitivity Low

Sensitivity

Decreased Sensitivity of very low and high frequencies [Daly 1993]

Spatial

Freq.

Contrast

Luminance

Masking Contrast

Sensitivity

Channel

Decomposition

Light

Scattering

…

…

. . . . . .

6 Frequency

Bands

6 Orientations Low Pass

Image

Cortex Transform [Watson 1987, Daly 1993]

Distortion Measures

Loss of Visible Contrast

REFERENCE

Contrast

Visibility

Threshold

Loss of Visible Contrast

TEST

REFERENCE

Contrast

Visibility

Threshold

Loss of Visible Contrast

Reference Test (Clipping)

Distortion map

Amplification of Invisible Contrast

REFERENCE

Contrast

Visibility

Threshold

Amplification of Invisible Contrast

TEST

REFERENCE

Contrast

Visibility

Threshold

Amplification of Invisible Contrast

Reference Distortion map* Test (Contouring)

*For clarity, visible

contrast loss is

not shown

Reversal of Visible Contrast

Contrast

REFERENCE

Reversal of Visible Contrast

Contrast

Visibility

Threshold

Visibility

Threshold

TEST

REFERENCE

Reversal of Visible Contrast

Reference Local contrast

reversal

No Structural Distortion

Visibility

Threshold

Visibility

Threshold

Visualization

Advantages over previous

metrics

Case Study

Local Gaussian Blur

HDR Test HDR Reference LDR Test LDR Reference

(1) HDR pair

HDR-VDP Our Metric SSIM

Loss

Amplification

Reversal

Distortion

(2) LDR pair

HDR-VDP Our Metric SSIM

Loss

Amplification

Reversal

Distortion

(3) HDR test, LDR reference

HDR-VDP Our Metric SSIM

Distortion Loss

Amplification

Reversal

(4) LDR test, HDR reference

HDR-VDP Our Metric SSIM

Loss

Amplification

Reversal

Distortion

Detecting distortions

HDR-VDP

SSIM

Sharpening Blur REFERENCE

Detecting “types” of distortions

Our

Method

Sharpening Blur REFERENCE

Loss

Amplification

Reversal

Applications

TMO Evaluation

FATTAL

PATTANAIK

Loss

Amplification

Reversal

REFERENCE

Inverse TMO Evaluation

Loss

Amplification

Reversal

REFERENCE LDR2HDR

Display Comparison (1) BrightSide DR37-P HDR Display (2000 cd/m2)

REFERENCE

Loss

Amplification

Reversal

Loss

Amplification

Reversal

Display Comparison (2) Barco Coronis 3MP LCD Display (400 cd/m2)

Loss

Amplification

Reversal

Display Comparison (3) Samsung SGH-D500 Cell Phone Display (30 cd/m2)

Summary

• Hybrid approach: HVS and structure

• Comparing different dynamic ranges

• Detecting “type” of distortions

• Applications on (inverse) tone mapping

and display comparison

• TODO

– Color

– Supra-Threshold

Luminance

Masking Contrast

Sensitivity

Light

Scattering

Decreased sensitivity due to glare around bright spots

[Deeley et al. 1991]

Channel

Decomposition