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Quality Assessment of Deblocked Images

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

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  1. Quality Assessment of Deblocked Images ChanghoonYim, Member, IEEE, and Alan Conrad Bovik, Fellow, IEEE

  2. Outline Introduction Quality assessment methods Simulation Results Concluding Remarks

  3. 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.

  4. Outline Introduction Quality assessment methods Simulation Results Concluding Remarks

  5. QUALITY ASSESSMENT • PSNR (Peak Signal-to-Noise Ratio) MSE PSNR

  6. QUALITY ASSESSMENT • SSIM (Structural Similarity) Luminance comparison function: l Contrast comparison function: c Structure comparison function: s

  7. QUALITY ASSESSMENT • SSIM SSIM l

  8. QUALITY ASSESSMENT • PSNR-B (PSNR Including Blocking Effects) BEFη． η , if , otherwise η．

  9. QUALITY ASSESSMENT • PSNR-B (PSNR Including Blocking Effects) MSE-B PSNR-B

  10. Outline Introduction Quality assessment methods Simulation Results Concluding Remarks

  11. Simulation Results Lena Peppers Babara

  12. Simulation Results PSNR comparison of images. Lena Peppers Babara Goldhill

  13. Simulation Results SSIM comparison of images. Lena Peppers Babara Goldhill

  14. Simulation Results PSNR-B comparison of images. Lena Peppers Babara Goldhill

  15. Simulation Results Study of H.264 In-loop filter Foreman Mother and Daughter

  16. Simulation Results PSNR comparison of filters for H.264 videos. Foreman Mother Hall Monitor Mobile

  17. Simulation Results SSIM comparison of filters for H.264 videos. Foreman Mother Hall Monitor Mobile

  18. Simulation Results PSNR-B comparison of filters for H.264 videos. Foreman Mother Hall Monitor Mobile

  19. Outline Introduction Quality assessment methods Simulation Results Concluding Remarks

  20. 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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