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Quality Assessment of Deblocked Images

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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. - PowerPoint PPT Presentation
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CHANGHOON YIM, MEMBER, IEEE, AND ALAN CONRAD BOVIK, FELLOW, IEEE Quality Assessment of Deblocked Images
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Page 1: Quality Assessment of  Deblocked  Images

CHANGHOON YIM, MEMBER, IEEE, AND ALAN CONRAD BOVIK, FELLOW, IEEE

Quality Assessment of Deblocked Images

Page 2: Quality Assessment of  Deblocked  Images

Outline

IntroductionQuality assessment methodsSimulation ResultsConcluding Remarks

Page 3: Quality Assessment of  Deblocked  Images

INTRODUCTION

Blocking effects are common in block-based image and video compression systems. Deblocking filter 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.

Page 4: Quality Assessment of  Deblocked  Images

Outline

IntroductionQuality assessment methodsSimulation ResultsConcluding Remarks

Page 5: Quality Assessment of  Deblocked  Images

QUALITY ASSESSMENT

PSNR (Peak Signal-to-Noise Ratio)

MSEPSNR

Page 6: Quality Assessment of  Deblocked  Images

QUALITY ASSESSMENT

SSIM (Structural Similarity)Luminance comparison function:

lContrast comparison function:

cStructure comparison function:

s

Page 7: Quality Assessment of  Deblocked  Images

QUALITY ASSESSMENT

SSIM

SSIM l

Page 8: Quality Assessment of  Deblocked  Images

QUALITY ASSESSMENT

PSNR-B (PSNR Including Blocking Effects)

BEFη.η , if , otherwise

η.

Page 9: Quality Assessment of  Deblocked  Images

QUALITY ASSESSMENT

PSNR-B (PSNR Including Blocking Effects)

MSE-BPSNR-B

Page 10: Quality Assessment of  Deblocked  Images

Outline

IntroductionQuality assessment methodsSimulation ResultsConcluding Remarks

Page 11: Quality Assessment of  Deblocked  Images

Simulation Results

Lena

Babara

Peppers

Page 12: Quality Assessment of  Deblocked  Images

Simulation Results

PSNR comparison of images.(a) Lena(b) Peppers(c) Babara(d) Goldhill

Page 13: Quality Assessment of  Deblocked  Images

Simulation Results

SSIM comparison of images.(a) Lena(b) Peppers(c) Babara(d) Goldhill

Page 14: Quality Assessment of  Deblocked  Images

Simulation Results

PSNR-B comparison of images.(a) Lena(b) Peppers(c) Babara(d) Goldhill

Page 15: Quality Assessment of  Deblocked  Images

Simulation Results

Study of H.264 In-loop filter

Foreman

Mother and Daughter

Page 16: Quality Assessment of  Deblocked  Images

Simulation Results

PSNR comparison of filters for H.264 videos.(a) Foreman(b) Mother(c) Hall Monitor(d) Mobile

Page 17: Quality Assessment of  Deblocked  Images

Simulation Results

SSIM comparison of filters for H.264 videos.(a) Foreman(b) Mother(c) Hall Monitor(d) Mobile

Page 18: Quality Assessment of  Deblocked  Images

Simulation Results

PSNR-B comparison of filters for H.264 videos.(a) Foreman(b) Mother(c) Hall Monitor(d) Mobile

Page 19: Quality Assessment of  Deblocked  Images

Outline

IntroductionQuality assessment methodsSimulation ResultsConcluding Remarks

Page 20: Quality Assessment of  Deblocked  Images

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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