1 / 13

Artifacts suppression in images and video

Artifacts suppression in images and video. Volodymyr Fedak. Introduction. What is the problem? Why is it important? What did I do? What are the results? So what next?. What is the problem?. blocking ringing blurring flickering. What is the problem?. F - 2. F - 1. F. F + 1. F + 2.

vicky
Download Presentation

Artifacts suppression in images and video

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Artifacts suppression in images and video Volodymyr Fedak

  2. Introduction • What is the problem? • Why is it important? • What did I do? • What are the results? • So what next?

  3. What is the problem? • blocking • ringing • blurring • flickering

  4. What is the problem? F - 2 F - 1 F F + 1 F + 2 Intra-frame processing… Inter-frame processing…

  5. Compressed information De-coder Artifact detection Reducing artifacts Transform to original format Enhanced information Coder parameters postprocessing Why is it important ? Postprocessing techniques: • spatial-temporal algorithmsalgorithms that transform signal to frequency domain • motion-compensated algorithmsiterative approaches based on the theory of projections onto convex set

  6. What did I do ? • Analyse modern postprocessing techniques • Implement most encouraging methods • Compare results of mentioned algorithms • Propose approaches for optimization

  7. Wavelet-based de-blocking and de-ringing algorithm proposed by Alan and Liew • Steps: • Detection of Block Discontinuities • Threshold Maps Generation at Different Wavelet Scales • low frequency filtering

  8. Non-Local Means NLM is an improvement of Bilateral filtering C(y, x) - geometric relationship S(I(y), I(x)) - luminance ratio I(y) – pixel luminance

  9. NLM could be presented: in general way: in terms of implementation: v(i) – noisy image W(i, j) - weighted average of pixels in the image v(j) – pixel luminance Non-Local Means N(x) - window surrounding pixel x; Q(x) is a search window around pixel x;

  10. Non-Local Means Parameters • h - determines the amount of averaging (h increases amount of blocking artifacts decrease). • N (x) – the match window/patch – when N(x) increases, blocking artifacts of the processed sequence decreases very slowly • Q(x) – the search window/patch – when Q(x) increases, artifacts of the processed sequence decreases very slowly for an increasing value of the search window size, and we have a large amount of computation time.

  11. Possible ways for optimization: • Extended NLM to the temporal domain . Use together with motion-compensation algorithm but apply some quality coefficient to the motion vector. • Add smart patch/search window size choosing algorithm. • Use Hierarchical block matching algorithm to find similar windows for speeding-up NLM

  12. Any questions ?

More Related