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Adaptive Denoising for Video Compression

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

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Adaptive Denoising for Video Compression

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  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 Wiener Adaptive LMMSE

  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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