CHANGHOON YIM, MEMBER, IEEE, AND ALAN CONRAD BOVIK, FELLOW, IEEE Quality Assessment of Deblocked Images.

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Presentation transcript:

CHANGHOON YIM, MEMBER, IEEE, AND ALAN CONRAD BOVIK, FELLOW, IEEE Quality Assessment of Deblocked Images

Outline Introduction Quality assessment methods Simulation Results Concluding Remarks

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.

Outline Introduction Quality assessment methods Simulation Results Concluding Remarks

QUALITY ASSESSMENT

Outline Introduction Quality assessment methods Simulation Results Concluding Remarks

Simulation Results Lena Babara Peppers

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

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

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

Simulation Results Study of H.264 In-loop filter Forema n Mother and Daughter

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

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

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

Outline Introduction Quality assessment methods Simulation Results 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.