Image Compression - SPECK 070711

8/4/2019 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



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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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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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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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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.



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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)

22Deptt. of ECE, Integral

University



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Temporal redundancy

Next frame




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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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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


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.


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


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


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


SPECKCoding :


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Conclusion

Cost of

compression

Cost of

Transmission and

storage

compromise


Media compression is indispensable even as storage and streaming capacities

increase


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Thank you

Deptt. of ECE, Integral University 37

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Image Compression - SPECK 070711