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

2. 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.

3. 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
minute180 MB storage. One 24-hour day262GB
 Using compression, 15 frames/second for 24
hour1.4 GB, 187 days of video could be stored
using the same disk space that uncompressed video
would use in one day.

4. 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.

5. Wavelet's properties :
Short time localized waves with zero integral
value.
Possibility of time shifting.
Flexibility.

6. 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.

7. Result
Original Image Compressed Image
Threshold: 3.5
Zeros: 42%
Retained
energy:
99.95%

8. Construction:
 We can construct discrete WT via iterated (octave-
band) filter banks
 The analysis section is illustrated below:
Level 1
Level 2
Level J
8

9. Construction by ICs:

10. 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

11. 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:
2
255
PSNR = 10 log10 MSE
-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 .

12. 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 schemea new method compute DWT
using integer arithmetic.
DWT has been implemented in hardware such as
ASIC and FPGA.

13. Advantage:
Future video/image compression
Improved low bit-rate compression performance
Improved lossless and lossy compression
Improved continuous-tone and bi-level
compression
Transmission in noisy environments
Robustness to bit-errors
Progressive transmission by pixel accuracy and
resolution
Protective image security

14. 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 time
Lower quality than JPEG at low compression rates

15. Applications:
Image communications and image data base
Video-surveillance systems.
High Quality videos with smaller size.
All the areas in which storage is matter of
concern.
Application in denoising.

16. 2-D WT Example
Boats image WT in 3 levels
16

17. WT-Application in Denoising
Boats image Noisy image (additive Gaussian noise)
17

18. WT-Application in Denoising
Boats image Denoised image using hard thresholding
18

19. 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.

20. ANY QUESTIONS???