Image Compression Model

8/7/2019 Image Compression Model

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(C)Lotus Argue Creation

IMAGEIMAGE

COMPRESSIONCOMPRESSIONMODELMODEL

SUBMITTED BY-

SUMANTA KUMAR DAS

0701218126

INFORMATION TECHNOLOGY

P.I.E.T

8TH

SEM




AGENDAAGENDA

y What is Image Compression?

y Why Image Compression?

y Image compression model

y Lossless and lossy compression

y Image Format

y JPEG Encoding

y Hoffman encoding

y Run length enoding

y conclusion




What is Image Compression?What is Image Compression?

y A technique used to reduce the volume of

information to be transmitted about an image

M

Width1 Unit

M

1/2

D

Height

1 Unit

1/2

M=Number

of Samples

D=Spacing

BetweenSamples




What is Image Compression?What is Image Compression?

(cont«.)(cont«.)y The goal of image compression is to

reduce the amount of data required to

represent a digital image.

y Important for reducing storage

requirements and improving

transmission rates.




Why image compression?Why image compression?

y Requirements may outstrip the anticipatedincrease of storage space and bandwidth

y For data storage and data transmission

DVD Real time applications

Printer

y Take advantage of :

Spatial redundancy Temporal redundancy (videos)




Image Compression

y Reducing the size of image data files

y While retaining necessary information

Original

Image

Compressed

Image file

extracted

Image filecompress decompress




Compression System Model

y Compression

Input Preprocessing EncodingCompressed

File

OutputPostprocessingDecodingCompressedFile

� Decompression




Image Compression Model





(cont·d)

y

y Mapper: transforms the input data into a

format that facilitates reduction of interpixel

redundancies.





(cont·d)

y

y Quantizer: reduces the accuracy of the

mapper·s output in accordance with some pre-

established fidelity criteria.





(cont·d)

y

y Symbol encoder: assigns the shortest code

to the most frequently occurring output

values.




Image Compression Models

(cont·d)

y

The inverse operations are performed.

y But « quantization is irreversible in

general.




Lossless orLossless or LossyLossy CompressionCompression

y Lossless compression

There is no information loss, and the image can bereconstructed exactly the same as the original

e.g RLE

Applications: Medical imagery, Archiving

y Lossy compression

Information loss is tolerable

e.g JPEG 2000

Applications: commercial distribution (DVD) and rateconstrained environment where lossless methods cannot provide enough compression ratio




Original image

Approximation of the

original image

Compact information (for

storage or transmission)

Lossycompression

Lossless

compression

encoding

decoding




Bitmap FormatBitmap Format

y Files with .bmp extension

y Each pixel is represented by a fixed number of

bits (integral power of 2)

y A typical BMP file usually contains the followingblocks of data:

o BMP Header Stores general information

o Bitmap Information Stores detailed information

o Color Palette Stores the definition of the colors

o Bitmap Data Stores the actual image, pixel by pixel.




Graphics Interchange FormatGraphics Interchange Format

y 256 colors out of a palette of 224 colors are

indexed

y Amongst the oldest formats available

y Supports animationsy Supports transparency and interlacing




Portable Network Graphics FormatPortable Network Graphics Format

y Developed to improve upon and replace GIF

y Supports 2D interlacing, transparency and

variable color depth

y Uses lossless data compression method calledDEFLATE

y Support transparency




JPEG JPEG

y Joint Photographic Expert·s Group

y International standard for photographs

y Lossless/lossy

y Based on the facts that :

Humans are more sensitive to lower spatial

frequency components

A large majority of useful image contentschange relatively slowly across images




JPEG Encoding JPEG Encoding

Cr

YCb Quantization

Fq(u, v)

DPCM

RLC

Entropy

Coding

Header

Tables

Data

Coding

Tables

Quant«

Tables

F(u, v)

8 x 8

DCTf(i, j)

8 x 8

Zig Zag

Scan




Steps involved :Steps involved :

y images are Divided into 8x8 blocks

y Each 8x8 block subject to DCT followed by

quantization

y Zig-zag scany DC coefficients stored using DPCM (digital

PCM)

y RLE used for AC coefficients

y Huffman encoding

y Frame generation




. . .

1x64

0 0 0 0 0 1 1 0 0 0 0 0

5,1

0 0

7,2

0 . . .2

45

54

48

45

9

-6

1x64

1x64

1x64

1x64

1x64

1x64

a. Zig-zag scan

b. DPCM of DC

coefficientsc. RLE of AC

coefficientsa.

b.

c.




DecodingDecoding

y Same steps as encoding are performed in

reverse

AdvantagesAdvantages� Compression ratios of 20:1 are easily attained

� 24-bits per pixel can be used leading to better

accuracy� Progressive JPEG(interlacing)




DisadvantagesDisadvantages

� Doesn·t support transparency

� Doesn·t work well with sharp edges, they

tend to blur unless high resolution is used

� Almost always lossy




y Huffman coding is based on statistical methods

and assigns variable length codes.

y It uses Binary encoding tree representing

commonly occurring values in few bits and less

commonly occurring values in more bits

Huffman Coding




Huffman Code: Example

Symbol Frequency

A 24

B 12

C 10

D 8

E 8 ----> total 186 bits (with 3

bit per code word)

62




Code Tree according to Huffman




Run-Length Coding

y Counting the number of adjacent pixels

with the same gray-level value

y Used primarily for binary image

y Mostly use horizontal RLC




Run-Length Coding(cont)

Binary Image 8X8

00000000

00101111

01100100

01001110

00111110

00000110

00001111

00000000

horizontal horizontal

1st Row 8

2nd Row 0,4,4

3rd Row 1,2,5

4th Row 1,5,2

5th Row 1,3,2,1,1

6th Row 2,1,2,2,1

7th Row 0,4,1,1,2

8th Row 8




Run-Length Coding(cont)

y Extending basic RLC to gray-level image byusing bit-plane coding

y It will better if change the natural code intogray code

1

0

1 00

0110

11

00

0111

10

Natural Gray Code




ConclusionConclusion

y So image compression helps to reduce the sizeof image and make compatible for storage

y Best tool for transmission image over internet

y

Image compression has less chance of loss of the image data

y Remove redundancies in image configuration




References :References :

y [1]. M. Shapiro, ́ Embedded image coding using zerotrees

of wavelet coefficientsµ, IEEE Transactions on Signal

y Processing, vol 41, pp 3445-3462, Dec 93.

y [2] Pankaj N. Topiwala, ́ Wavelet image and video

compressionµ, 1998 Edition

y [3] Google search

y [4] Dr.Muhammad Younis Javed and Mr.Abid

Nadeem,µData Compression Through Huffman

Codingµ,IEEE 2000, Vol 2, pp.187-190.




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Image Compression Model

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