02-07-2013, 02:51 PM
WAVELET VIDEO PROCESSING TECHNOLOGY
WAVELET VIDEO.pptx (Size: 1.9 MB / Downloads: 32)
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.
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.
Compression Example:
A two dimensional (image) compression, using 2D wavelets analysis.
The image is a Fingerprint.
FBI uses a wavelet technique to compress its fingerprints database.
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 .
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.
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
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.