wavelet video processing tecnology

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WAVELET VIDEO PROCESSING TECHNOLOGY BY- PRASHANT KUMAR SINGH ECE-’D’ 0902731073

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Page 1: Wavelet video processing tecnology

WAVELET VIDEO PROCESSING TECHNOLOGY

BY-PRASHANT KUMAR SINGHECE-’D’0902731073

Page 2: Wavelet video processing tecnology

What is Wavelet Analysis ?

And…what is a wavelet…?

A wavelet is a waveform of effectively limited duration that has an average value of zero.

Page 3: Wavelet video processing tecnology

Need for Compression:

Transmission and storage of uncompressed video would be extremely costly and impractical

Frame with 352x288 contains 202,752 bytes of info. Recoding of uncompressed version of this video at 15

frames per second would require 3 MB. One minute180 MB storage. One 24-hour day262GB

Using compression, 15 frames/second for 24 hour1.4 GB, 187 days of video could be stored using the same disk space that uncompressed video would use in one day.

Page 4: Wavelet video processing tecnology

Discrete Wavelet Transform:

The wavelet transform (WT) has gained widespread acceptance in signal processing and image compression.

Because of their inherent multi-resolution nature, wavelet-coding schemes are especially suitable for applications where scalability and tolerable degradation are important

Recently the JPEG committee has released its new image coding standard, JPEG-2000, which has been based upon DWT.

Page 5: Wavelet video processing tecnology

Wavelet's properties :

Short time localized waves with zero integral value.

Possibility of time shifting.

Flexibility.

Page 6: Wavelet video processing tecnology

Compression Example:

A two dimensional (image) compression, using 2D wavelets analysis.

a) The image is a Fingerprint.b) FBI uses a wavelet technique to compress

its fingerprints database.

Page 7: Wavelet video processing tecnology

Result

Original Image Compressed Image

Threshold: 3.5Zeros: 42%Retained energy:99.95%

Page 8: Wavelet video processing tecnology

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Construction:

We can construct discrete WT via iterated (octave-band) filter banks

The analysis section is illustrated below:

Level 1

Level 2

Level J

Page 9: Wavelet video processing tecnology

Construction by ICs:

Page 10: Wavelet video processing tecnology

Visual Comparison:

(a) (b) (c)

(a) Original Image256x256Pixels, 24-BitRGB (b) JPEG (DCT) Compressed with compression ratio 43:1(c) JPEG2000 (DWT) Compressed with compression ratio 43:1

Page 11: Wavelet video processing tecnology

Performance:

Peak Signal to Noise ratio used to be a measure of image quality .

The PSNR between two images having 8 bits per pixel or sample in terms of decibels (dBs) is given by:

PSNR = 10 log10

-mean square error (MSE) Generally when PSNR is 40 dB or greater, then the

original and the reconstructed images are virtually indistinguishable by human observers .

MSE

2255

Page 12: Wavelet video processing tecnology

Implementation Complexity:

The complexity of calculating wavelet transform depends on the length of the wavelet filters, which is at least one multiplication per coefficient.

EZW, SPHIT use floating-point demands longer data length which increase the cost of computation.

Lifting schemea new method compute DWT using integer arithmetic.

DWT has been implemented in hardware such as ASIC and FPGA.

Page 13: Wavelet video processing tecnology

Advantage:

Future video/image compressionImproved low bit-rate compression performanceImproved lossless and lossy compressionImproved continuous-tone and bi-level

compressionTransmission in noisy environmentsRobustness to bit-errorsProgressive transmission by pixel accuracy and

resolutionProtective image security

Page 14: Wavelet video processing tecnology

Disadvantages:

The cost of computing DWT as compared to DCT may be higher.

The use of larger DWT basis functions or wavelet filters produces blurring and ringing noise near edge regions in images or video frames

Longer compression timeLower quality than JPEG at low compression rates

Page 15: Wavelet video processing tecnology

Applications:

Image communications and image data baseVideo-surveillance systems.High Quality videos with smaller size.All the areas in which storage is matter of

concern.Application in denoising.

Page 16: Wavelet video processing tecnology

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2-D WT Example

Boats image WT in 3 levels

Page 17: Wavelet video processing tecnology

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WT-Application in Denoising

Boats image Noisy image (additive Gaussian noise)

Page 18: Wavelet video processing tecnology

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WT-Application in Denoising

Boats image Denoised image using hard thresholding

Page 19: Wavelet video processing tecnology

Conclusion:

Wavelet-based coding provides substantial improvement in picture quality at low bit rates.

Interaction of harmonic analysis with data compression, joint source channel coding, image coding based on models of human perception, scalability robustness, error resilience, and complexity are a few of the many outstanding challenges in image coding to be fully resolved and may affect image data compression performance in the years to come.

Page 20: Wavelet video processing tecnology

ANY QUESTIONS???