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Adaptive Denoising for Video Compression

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Adaptive Denoising for Video Compression Eren Soyak EECS 463 Winter 2006 Northwestern University Video Compression and You Demand for video where no video has gone ... – PowerPoint PPT presentation

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Title: Adaptive Denoising for Video Compression


1
Adaptive Denoisingfor Video Compression
  • Eren Soyak
  • EECS 463
  • Winter 2006
  • Northwestern University

2
Video Compression and You
  • Demand for video where no video has gone before

Source
Source
You
Source
3
Video Compression and You
  • Demand for video where no video has gone before

Source
Source
You
Encode
Medium
Decode
Source
4
Video Compression and You
  • Demand for video where no video has gone before

Pos t proces sing
Preproces sing
Source
Source
You
Encode
Channel
Decode
Source
5
Video and Compression
  • Video compression works by identifying and
    exploiting redundancy in source video
  • The more information there is in the source, the
    more difficult it is to compress into a smaller
    form

Foreman
Foreman.264
6
Noise and Compression
  • Noise is usually present in source video due to
    various reasons (capture, film grain,
    quantization, transmission errors etc)
  • Wide spectrum noise is very difficult to compress

The ever-popular AWGN-type noise
Deprecated old analog-type noise
7
Dealing with Noise
  • Pre/post filtering methods very useful
  • Simple denoising method averaging filter

3 pels
5 pels
7 pels
8
Good, Bad and Ugly Denoising
  • Denoising must distinguish between original
    signal and noise, filter out only the noise.
    Prediction of the noise and/or the original video
    is usually required for this.
  • Smoothing, edge loss and blurring are all
    undesirable

Despeckle
Smart blur
10 pel average
9
Case Study AWGN
  • Additive White Gaussian Noise (AWGN) can be
    introduced by capture devices, especially due to
    poor lighting and sometimes weather.
  • AWGN breaks most compression algorithms.
  • Consider signal independent AWGN.

Foreman AWGN
10
Advanced Denoising (Wiener)
  • The Wiener filter is commonly used by the
    ambitious for generic denoising.
  • Requires little information about noise.
  • Few catastrophic corner cases.

Wiener(Foreman AWGN)
11
Global Denoising Issues
  • The visibility (and usually compression
    hindrance) of noise is a function of the source
    even if the severity of the noise itself is not
    noise is more visible on smooth regions as
    opposed to texture.
  • It would be highly desirable to filter noise such
    that the final video retains local shape/texture
    characteristics as well.
  • Adaptive methods begin to suggest themselves.

12
LMMSE Filtering
  • Linear Minimum Mean Squared Error filter (IIR)

(1)
Noisy image
LMMSE estimate of ideal image s(n1, n2)
Impulse response
13
The Unrealizable Wiener Filter
  • The principle of orthogonality states that the
    estimation error s(n1, n2)- (n1, n2) should be
    orthogonal to every sample of the observed image.

(2)
14
The Impossible Wiener IR
  • Substituting (1) into (2) and simplifying we can
    express the the impulse response of the filter as
    a 2D convolution
  • Is impossible to realize since infinite time is
    required before an output sample is computed.

autocorrelation of observations
cross correlation between ideal and observed image
Discrete Wiener-Hopf equation
15
Adaptive LMMSE
  • Kuan et al. proposed in 85

local mean
observation
local variance
filtered output
estimated noise variance
16
Adaptive LMMSE Performance
  • Given its adaptive nature to local image
    properties the filter is better at preserving
    edges/texture while removing noise.
  • It is very process-intensive and sensitive to
    misestimation of noise variance.

Adaptive LMMSE(Foreman AWGN)
17
Comparing Filter Outputs
18
Comparing Filter Outputs
Adaptive LMMSE
Wiener
19
Comparing Compressed Video
  • Compressed at 512 kbps at H.264 Main Profile

Adaptive LMMSE
Wiener
20
Weighed Adaptive LMMSE
  • Directionally weighed variance matrix
  • May better account for edges due to 2D direction
    component
  • Choice of weight matrix could be optimized

1 2 1 2 3 2 1 2 1
21
Weighed Adaptive LMMSE
  • Prone to blurring if matrix weights poorly
    chosen..

Poorly Weighed Adaptive LMMSE(Foreman AWGN)
22
Bibliography
  • A. Murat Tekalp, Digital Video Processing, 95
  • J. S. Lim, Two Dimensional Signal and Image
    Processing, 90
  • D.T. Kuan, A.A. Sawchuk, T.C. Strand, P. Chavel,
    Adaptive noise smoothing filter for images with
    signal-dependent noise, 85
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