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Quality Assessment of Deblocked Images. Changhoon Yim , Member, IEEE, and Alan Conrad Bovik , Fellow, IEEE. Outline. Introduction Quality assessment methods Simulation Results Concluding Remarks. INTRODUCTION.

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Quality assessment of deblocked images

Quality Assessment of Deblocked Images

ChanghoonYim, Member, IEEE, and Alan Conrad Bovik, Fellow, IEEE


Outline
Outline

Introduction

Quality assessment methods

Simulation Results

Concluding Remarks


Introduction
INTRODUCTION

Blocking effects are common in block-based image and video compression systems.Deblockingfilter can improve image quality in some aspects, but can reduce image quality in other regards.

In this paper, we will review the image quality assessment methods, and present the simulation results on quality assessment of deblocked images and videos.

It also propose a new deblocking quality index, PSNR-B.


Outline1
Outline

Introduction

Quality assessment methods

Simulation Results

Concluding Remarks


Quality assessment
QUALITY ASSESSMENT

  • PSNR (Peak Signal-to-Noise Ratio)

    MSE

    PSNR


Quality assessment1
QUALITY ASSESSMENT

  • SSIM (Structural Similarity)

    Luminance comparison function:

    l

    Contrast comparison function:

    c

    Structure comparison function:

    s


Quality assessment2
QUALITY ASSESSMENT

  • SSIM

    SSIM l


Quality assessment3
QUALITY ASSESSMENT

  • PSNR-B (PSNR Including Blocking Effects)

    BEFη.

    η , if

    , otherwise

    η.


Quality assessment4
QUALITY ASSESSMENT

  • PSNR-B (PSNR Including Blocking Effects)

    MSE-B

    PSNR-B


Outline2
Outline

Introduction

Quality assessment methods

Simulation Results

Concluding Remarks


Simulation results
Simulation Results

Lena

Peppers

Babara


Simulation results1
Simulation Results

PSNR comparison

of images.

Lena

Peppers

Babara

Goldhill


Simulation results2
Simulation Results

SSIM comparison

of images.

Lena

Peppers

Babara

Goldhill


Simulation results3
Simulation Results

PSNR-B comparison

of images.

Lena

Peppers

Babara

Goldhill


Simulation results4
Simulation Results

Study of H.264 In-loop filter

Foreman

Mother and Daughter


Simulation results5
Simulation Results

PSNR comparison of filters for H.264 videos.

Foreman

Mother

Hall Monitor

Mobile


Simulation results6
Simulation Results

SSIM comparison of filters for H.264 videos.

Foreman

Mother

Hall Monitor

Mobile


Simulation results7
Simulation Results

PSNR-B comparison of filters for H.264 videos.

Foreman

Mother

Hall Monitor

Mobile


Outline3
Outline

Introduction

Quality assessment methods

Simulation Results

Concluding Remarks


Concluding remarks
Concluding Remarks

PSNR-B modifies the conventional PSNR by including an effective blocking effect factor. The simulation results show that PSNR-B results in better performance than PSNR for image quality assessment of these impaired images.

Quality studies of this type using special-purpose quality indices (such as PSNR-B) and perceptually proven indices (such SSIM) in conjunction are of considerable value, not only for studying deblocking operations, but also for other image improvement applications, such as restoration, denoising, enhancement, and so on.


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