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A Perceptually Relevant Approach to

Ringing Region Detection

Hantao Liu

Nick Klomp

Prof. Dr. Ingrid Heynderickx

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Introduction

Objective Metrics

Computational models for the evaluation of image quality

A Ringing Metric

To quantify the annoyance of ringing artifacts in compressed images

Alternatives for expensive image quality assessment by human subjects

Our main focus is on image and video compression and transmission

We consider realistic distortions that arise from compression or transmission,

e.g. blockiness, ringing, blur, and white noise

We consider the no-reference approach, where the assessment is based onthe compressed image itself only

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Introduction

What is Ringing?

Lossy

Compression

Synthetic pattern

Ringing results from the high frequency component loss due to quantization

Ringing

Ringing

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Introduction

What is Ringing?

Compression

Ringing

Ringing

3-D illustration

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Introduction

What is Ringing?

Real-life compressed image

Ringing

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Introduction

Challenges for a Ringing Metric

Signal dependent noise

Ringing Detection

Spatial location of visible ringing artifacts

Ringing only occurs around high contrast edgesin compressed images

Spatial maskingof the human visual system (HVS)

Luminance masking: very dark or very bright areas

Texture masking: textured or detailed areas

In agreement with human perception

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Proposed Algorithm

Extraction of Perceptual Edges

Existing methods usually employ an ordinary edge detector (e.g. Sobel)

An optimal threshold cannot be obtained

A perceptually more meaningful edge detector is needed

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Proposed Algorithm

Extraction of Perceptual Edges

Smoothing the image until textual details are significantly reduced

Gaussian Smoothing

Edge Detector

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Proposed Algorithm

Extraction of Perceptual Edges

Edge-preserving smoothing is needed

BilateralFiltering

Line Segment(LS)

Perceptual Edge Map

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Proposed Algorithm

Detection of Perceived Ringing Regions

Local Region Classification:(1) Edge Region (i.e. EdReg)

(2) Detection Region (i.e. DeReg)

(3) Feature Extraction Region (i.e. FeXReg)

Morphological Operation

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Proposed Algorithm

Detection of Perceived Ringing Regions

Texture masking

Human Vision Model: Ringing visibility to the human eye

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Proposed Algorithm

Detection of Perceived Ringing Regions

Luminance masking

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Proposed Algorithm

Detection of Perceived Ringing Regions

Implementation of texture masking

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Proposed Algorithm

Detection of Perceived Ringing Regions

Implementation of luminance masking

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Proposed Algorithm

Detection of Perceived Ringing Regions

Computational ringing region (CRR) map

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Psychovisual Experiment

Subjective Experiment

To find out where human beings perceive ringing in natural images

Visible ringing regions in compressed images were marked by human subjects

Eight full-color images compressed with JPEG at two compression levels (i.e.16 stimuli) were assessed by 12 subjects (8 males and 4 females)

Subjective ringing region (SRR) map

MRR Map (m=12) SRR Map (Thr=1/2)Caps (Q=25)

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Performance Evaluation

The computational ringing region (CRR) map predicted by our proposedmethod is compared to the subjective ringing region (SRR) map derived from

the subjective experiment

CRR MapSRR Map

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Performance Evaluation

The correlation between the CRR and SRR maps is evaluated visually in acomparison map (RGB color image):

green: correlated ringing regionsred: uncorrelated ringing regions (from CRR)blue: uncorrelated ringing regions (from SRR)

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Conclusions

A novelringing region detection method is proposed

It shows a strong correlationwith subjective data (see [1])

Current work includes calculating the quantitative correlationbetween theCRR map and SRR map, and comparing our model with alternativesin literature

Current work includes applying our detection method to a ringing metric

[1] H. Liu, N. Klomp and I. Heynderickx, "Perceptually Relevant Ringing Region DetectionMethod", EUSIPCO2008 The 16th European Signal Processing Conference, August 2008.

Reference