image compression - speck 070711
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Presentation on
Image Compression
By : N R Kidwai
1Deptt. of ECE, Integral University
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One picture is worth more than ten thousand words
Anonymous
2Deptt. of ECE, Integral University
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What is a Digital Image?
Adigital image is a representation of a two-dimensional image as a finite set ofdigital values, called picture elements or pixels. Pixel values typically represent
gray levels, colours, heights, opacities etc
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What is a Digital Image? (cont)
Common image formats include:
1 sample per pixel (B&W or Grayscale)
3 samples per pixel (Red, Green, and Blue)
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What is Digital Image Processing?
Digital image processing focuses on two major tasks
- Improvement of pictorial information for human
interpretation
- Processing of image data for storage, transmission
and representation for autonomous machine
perception
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History of Digital Image Processing
Early 1920s: One of the first applications of digital
imaging was in the news-paper industry.
The Bartlane cable picture transmission service
Images were transferred by submarine cable
between London and New York
Pictures were coded for cable transfer and
reconstructed at the receiving end on a
telegraph printer
Early digital image
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History of Digital Image Processing (cont)
Mid to late 1920s: Improvements to the Bartlanesystem resulted in higher quality images
New reproduction processes based on
photographic techniques
Increased number of tones in reproduced
images
Improved
digital image
Early 15 tone digital
image
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History of Digital Image Processing (cont)
1960s: Improvements in computing technology andthe onset of the space race led to a surge of work
in digital image processing
1964: Computers used to improve the
quality of images of the moon taken by
the Ranger 7probe
Such techniques were used in other space
missions including the Apollo landingsA picture of the moon taken
by the Ranger 7 probe
minutes before landing
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History of Digital Image Processing (cont)
1970s: Digital image processing begins to beused in medical applications
1979: Sir Godfrey N. Hounsfield & Prof.
Allan M. Cormack share the Nobel
Prize in medicine for the invention of
tomography,the technology behind Computerised
Axial Tomography (CAT) scans
Typical head slice CAT
image
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History of Digital Image Processing (cont)
1980s - Today: The use of digital image processing techniques has exploded andthey are now used for all kinds of tasks in all kinds of areas
Image enhancement/restoration
Artistic effects
Medical visualisation
Industrial inspection
Law enforcement
Human computer interfaces
1984
Canon demonstrates first digital electronic still camera.
1985
Pixar introduces digital imaging processor.
10Deptt. of ECE, Integral University
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Applications of DIP: Image Enhancement
One of the most common uses of DIP techniques: improve quality, remove noise etc
Hubble telescope(1990)
However, an incorrect mirror
made many of
Hubbles images useless
Image processing
techniques were used to fix
this
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Applications of DIP
Examples: MedicineTake slice from MRI scan of canine heart, and find boundaries between types of
tissue
Image with gray levels representing tissue density
Use a suitable filter to highlight edges
Original MRI Image of a Dog Heart Edge Detection Image
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Applications of DIP : GIS
Geographic Information SystemsDigital image processing techniques are used extensively to manipulate
satellite imagery
Terrain classification
Meteorology
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Applications of DIP : GIS (cont)
Night-Time Lights of the Worlddata set
Global inventory of human
settlement
Not hard to imagine the kind of
analysis that might be done using
this data
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Applications of DIP : PCB Inspection
Printed Circuit Board (PCB) inspectionMachine inspection is used to determine that all components are present
and that all solder joints are acceptable
Both conventional imaging and x-ray imaging are used
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Applications of DIP : Law Enforcement
Image processing techniques are used
extensively by law enforcers
Number plate recognition for speed
cameras/automated toll systems
Fingerprint recognition
Face recognitionGesture recognition
Enhancement of CCTV images
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Key Stages in Digital Image Processing:
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem
Domain Colour Image
ProcessingImage
Compression
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Image representation :
An image: 2D array of pixels (rows, columns)A pixel value:
Greyscale/intensity: [8 bits]
Color: [3 x 8 bits] [Red, Green, Blue] [R,G,B]
Data: Greyscale: 300x300 = 90,000pixels = 90Kbyte = 720Kbit
Color: x3 = 270Kbyte = 2.16Mbit
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Why Do We Need Compression?
Image compression aims to reduce-data storage space
-data transmission cost
EX. 1 minute full motion video480x640 pixels
24 bit for color
25 frames per second
60 second
480x640x24x25x60 =10.29 G bits = 1.28 GB
Transmission time @ 1Mbps = 10546 second = 175.7 min
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What is Image Compression?
Reduction of the amount of data required to represent a digital image removal of redundant data.
Transforming a 2-D pixel array into a statistically uncorrelated data set
Application of Image Compression:
Important in data storage and data transmission
Examples:
Progressive transmission of images (Internet)
Video coding (HDTV, teleconferencing)
Digital libraries and image databases
Remote sensing
Medical imaging
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How to achieve compression?
Minimizing the redundancy in the image.
Data Redundancy
Data are the meansby which information is conveyed
Various amounts of data may be used to represent the same amount of information
Redundancy
Interpixelredundancy
Psychovisualredundancy
Temporalredundancy
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Spatial redundancy (Interpixel redundancy)
The value of any given pixel can be
reasonablypredicted from the values of its
neighbors; as a consequence, any pixelcarries a small amount of information
Interpixel redundancy can be reduced
through mappings (e.g., differences between
adjacentpixels)
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University
How to achieve compression?
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Temporal redundancy
Next frame
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How to achieve compression?
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How to achieve compression?
Phyco visual Redundancy-The eye does not respond with equal sensitivity to all visual information
-Certain information has less relative importance than other information in normal
visual processing (psychovisually redundant)
-It can be eliminated without significantly impairing the quality of image perception
-Less sensitive to high and low spatial frequency than to mid-spatial frequency.
-Sensitivity to quantizing distortion decreases with increasing luminance levels.
(noise masking property).
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How to achieve compression?
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Image Compression Techniques
Image Compression Scheme
Pixel coding Prediction Transform Hybrid
Runlength
Huffman
DPCM
ADPCM
DM
DCT
DWT
JPEG
JPEG2000
SPIHT
SPECK
LTW
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Loss less Vs. Lossy compression
Lossless (or information-preserving) compression:
Images can be compressed and restored without any loss of information (e.g.,medical imaging, satellite imaging)
Lossless compression tools
Entropy coding : Huffman, Arithmetic, Lempel-Ziv, run-length
Predictive coding: reduce the dynamic range to code
Transform: enhance energy compaction
Lossy compression:
Perfect recovery is not possible but provides a large data compression (e.g., TV
signals, teleconferencing)Lossy compression tools
Discarding and thresholding
Quantization: Scalar quantization and vector quantization
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Transform Coding
- transform image
- code the coefficients of the transform
- transmit them
- reconstruct by inverse transform
Benefits
- transform coeff. relatively uncorrelated
- energy is highly compacted
- reasonable robust relative to
channel errors
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Transform Coding
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Wavelet Image Compression
Wavelet Transform :
-Time frequency analysis approach-Use simple basis functions
-decompose signal into coefficients assigned to
basis functions
-Quantization error does not remain localized
-Optimal for images containing sharp edges, orcontinuous curves/lines (fingerprints)
-Compared with DCT, uses more optimal set offunctions to represent sharp edges than cosines.
-Wavelets are finite in extent as opposed tosinusoidal functions
-The standard use separable 1-D DWT forimplementation
Several different families of
wavelets.
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Wavelet vs. JPEG compression
Wavelet compression
file size: 1861 bytes
compression ratio - 105.6
JPEG compression
file size: 1895 bytes
compression ratio - 103.8
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Example of 2-D W.T.
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Three level dyadic wavelet decomposition of Image Lena
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SPECKCoding :BIT PLA NE ENCODING
a b c d e f
f
e
d
c
b
a
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63 -34 49 10 7 13 -12 7
-31 23 14 -13 3 4 6 -1
15 14 3 -12 5 -7 3 9
-9 -7 14 8 4 -2 3 2
-5 9 -1 47 4 6 -2 2
3 0 -3 2 3 -2 0 4
2 -3 6 -4 3 6 3 6
5 11 5 6 0 3 -4 4
1 1 1 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
1 0 1 0 0 0 0 0
1 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
1 0 0 1 0 1 1 0
1 0 1 1 0 0 0 0
1 1 0 1 0 0 0 1
1 0 1 1 0 0 0 0
0 1 0 1 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
1 0 0 0 1 1 1 1
1 1 1 1 0 1 1 0
1 1 0 1 1 1 0 0
0 1 1 0 1 0 0 0
1 0 0 1 1 1 0 0
0 0 0 0 0 0 0 1
0 0 1 1 0 1 0 1
1 0 1 1 0 0 1 1
1 1 0 1 1 0 0 1
1 1 1 0 1 0 1 0
1 1 1 0 0 1 1 0
0 1 1 0 0 1 1 1
0 0 0 1 0 1 1 1
1 0 1 1 1 1 0 0
1 1 1 0 1 1 1 1
0 1 0 1 0 1 0 0
1 0 1 0 1 1 0 1
1 1 0 1 1 0 0 1
1 0 1 0 1 1 1 1
1 1 0 0 0 0 1 0
1 1 1 1 0 0 0 0
1 0 1 0 1 0 0 0
0 1 0 0 1 0 1 0
1 1 1 0 0 1 0 0
1 -1 1 1 1 1 -1 1
-1 1 1 -1 1 1 1 -1
1 1 1 -1 1 -1 1 1
-1 -1 1 1 1 -1 1 1
-1 1 -1 1 1 1 -1 1
1 1 -1 1 1 -1 1 1
1 -1 1 -1 1 1 1 1
1 1 1 1 1 1 -1 1
33Deptt. of ECE, Integral University
SPECKCoding :
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Set Partitioning Embedded Coder: SPECK
Partitions and codes sets of coefficients grouped within subbands.
Can encode any grouping of pixels or coefficients
Operate through significance decisions for partitioning sets of transform coefficients
Uses quadrature partitioning. and octave band partitioning.
SPECK use two lists
34Deptt. of ECE, Integral University
SPECKCoding :
SS
I I
SS S
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Key features of SPECK Coding-fully bit-embedded
(next bit conveys less value information than previous One)
-Low complexity
- Less list memory required
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SPECKCoding :
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Conclusion
Cost of
compression
Cost of
Transmission and
storage
compromise
36Deptt. of ECE, Integral University
Media compression is indispensable even as storage and streaming capacities
increase
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Thank you
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