volodymyr fedak artifacts suppression in images and video
TRANSCRIPT
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Volodymyr Fedak
Artifacts suppression in images and video
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Introduction
What is the problem?
Why is it important?
What did I do? What are the results?
So what next?
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What is the problem?
blocking
ringing
blurring
flickering
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What is the problem?
F - 2F - 1
F
F + 1F + 2
Intra-frame processing… Inter-frame processing…
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Why is it important ?
De-coder Artifact detection
Reducing artifacts
Transform to original format
Enhanced information
postprocessingCoder parameters
Compressed information
Postprocessing techniques:•motion-compensated algorithms
iterative approaches based on the
theory of projections onto convex set •spatial-temporal algorithms
algorithms that transform signal
to frequency domain
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What did I do ?
Analyse modern postprocessing techniques
Implement most encouraging methods
Compare results of mentioned algorithms
Propose approaches for optimization
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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
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Non-Local Means
NLM is an improvement of Bilateral filtering
dyxIyIsxycyIxI ))(),((),()()(
C(y, x) - geometric relationship
S(I(y), I(x)) - luminance ratio
I(y) – pixel luminance
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Non-Local Means
NLM could be presented:in general way:
)(),())(( jvjiwivNLIj
v(i) – noisy imageW(i, j) - weighted average of pixels in the image v(j) – pixel luminance
in terms of implementation:
2
2)()(
)(
)()(
1 h
yNxN
xQyh exz
xCxNL
)(
)()(
2
2
2
)(xQy
h
yNxN
exC
N(x) - window surrounding pixel x;Q(x) is a search window around pixel x;
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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.
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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
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Any questions ?