Image compression
This presentation is the property of its rightful owner.
Sponsored Links
1 / 28

Image Compression PowerPoint PPT Presentation


  • 159 Views
  • Uploaded on
  • Presentation posted in: General

Image Compression. อ.รัชดาพร คณาวงษ์ วิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยศิลปากรวิทยาเขตพระราชวังสนามจันทร์. Image Compression. Reducing the size of image data files While retaining necessary information. Original Image. Compressed Image file. extracted Image file. compress.

Download Presentation

Image Compression

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Image compression

Image Compression

อ.รัชดาพร คณาวงษ์

วิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์

มหาวิทยาลัยศิลปากรวิทยาเขตพระราชวังสนามจันทร์


Image compression1

Image Compression

  • Reducing the size of image data files

  • While retaining necessary information

Original Image

Compressed Image file

extracted Image file

compress

decompress


Terminology

Terminology

  • refer relation between original image and

    the compressed file

  • Compression Ratio

  • Bits per Pixel

A larger number implies a better compression

A smaller number implies a better compression


Compression ratio

Compression Ratio

(1)

Ex Image 256X256 pixels, 256 level grayscale can be compressed file size 6554 byte.

Original Image Size = 256X256(pixels) X 1(byte/pixel)

= 65536 bytes


Bits per pixel

(2)

Bits per Pixel

Ex Image 256X256 pixels, 256 level grayscale can be compressed file size 6554 byte.

Original Image Size = 256X256(pixels) X 1(byte/pixel)

= 65536 bytes

Compressed file = 6554(bytes)X8(bits/pixel)

= 52432 bits


Why we want to compress

Why we want to compress?

To transmit an RGB 512X512, 24 bit image

via modem 28.2 kbaud(kilobits/second)


Key of compression

Key of compression

  • Reducing Data but Retaining Information

DATA are used to convey information.

Various amounts of data can be used to represent the same amount of information. It’s “Data redundancy”

Relative data redundancy


Entropy

Entropy

  • Average information in an image.

  • Average number of bits per pixel


Redundancy

Redundancy

  • Coding Redundancy

  • Interpixel Redundancy

  • Psychovisual Redundancy


Coding redundancy

Coding Redundancy

  • Occurred when data used to represent image are not utilized in an optimal manner


Coding redundancy cont

Coding Redundancy(cont)

  • An 8 gray-level image distribution shown in Table


Coding redundancy cont1

Coding Redundancy(cont)

  • Original Image 8 possible gray level = 23


Interpixel redundancy

Interpixel Redundancy

  • Adjacent pixel values tend to be highly correlated


Psychovisual redundancy

Psychovisual Redundancy

  • Some information is more important to the human visual system than other types of information


Compression system model

Compressed

File

Preprocessing

Encoding

Input

Compressed

File

Decoding

Postprocessing

Output

Compression System Model

  • Compression

  • Decompression


Types of compression

Types of Compression

There are 2 types of Compression

  • Loseless Compression

  • Lossy Compression


Loseless compression

Loseless Compression

  • No data are lost

  • Can recreated exactly original image

  • Often the achievable compression is mush less


Huffman coding

Huffman Coding

  • Using Histogram probability

  • 5 Steps

    • Find the histogram probabilities

    • Order the input probabilities(smalllarge)

    • Addition the 2 smallest

    • Repeat step 2&3, until 2 probability are left

    • Backward along the tree assign 0 and 1


Huffman coding cont

40

30

20

10

0 1 2 3

Huffman Coding(cont)

  • Step 1 Histogram Probability

p0 = 20/100 = 0.2

p1 = 30/100 = 0.3

p2 = 10/100 = 0.1

p3 = 40/100 = 0.4

  • Step 2 Order

p3 0.4

p1 0.3

p0 0.2

p2 0.1


Huffman coding cont1

Huffman Coding(cont)

  • Step 3 Add 2 smallest


Huffman coding cont2

Huffman Coding(cont)

  • The original Image :average 2 bits/pixel

  • The Huffman Code:average


Run length coding

Run-Length Coding

  • Counting the number of adjacent pixels with the same gray-level value

  • Used primarily for binary image

  • Mostly use horizontal RLC


Run length coding cont

Run-Length Coding(cont)

Binary Image 8X8

horizontal


Run length coding cont1

1

0

1

Run-Length Coding(cont)

  • Extending basic RLC to gray-level image by using bit-plane coding

  • It will better if change the natural code into gray code

00

01

10

11

00

01

11

10

Natural

Gray Code


Lempel ziv weich coding lzw

Lempel-Ziv-Weich Coding(LZW)

  • Assign fixed-length code words to variable

  • GIF,TIFF,PDF


Lossy compression

Lossy Compression

  • Allow a loss in the actual image data

  • Can not recreated exactly original image

  • Commonly the achievable compression is mush more

  • JPEG


Fidelity criteria

Fidelity Criteria

  • Objective fidelity criteria

    • RMS Error

    • RMS Signal-To-Noise Ratio

  • Subjective fidelity criteria


Image compression

JPEG


  • Login