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## PowerPoint Slideshow about 'Image Compression' - phylicia

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### 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

decompress

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

(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

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?

To transmit an RGB 512X512, 24 bit image

via modem 28.2 kbaud(kilobits/second)

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

Redundancy

- Coding Redundancy
- Interpixel Redundancy
- Psychovisual Redundancy

Coding Redundancy

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

Coding Redundancy(cont)

- An 8 gray-level image distribution shown in Table

Coding Redundancy(cont)

- Original Image 8 possible gray level = 23

Interpixel Redundancy

- Adjacent pixel values tend to be highly correlated

Psychovisual Redundancy

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

File

Preprocessing

Encoding

Input

Compressed

File

Decoding

Postprocessing

Output

Compression System Model- Compression

- Decompression

Loseless Compression

- No data are lost
- Can recreated exactly original image
- Often the achievable compression is mush less

Huffman Coding

- Using Histogram probability
- 5 Steps
- Find the histogram probabilities
- Order the input probabilities(smalllarge)
- Addition the 2 smallest
- Repeat step 2&3, until 2 probability are left
- Backward along the tree assign 0 and 1

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(cont)

- Step 3 Add 2 smallest

Huffman Coding(cont)

- The original Image :average 2 bits/pixel
- The Huffman Code:average

Run-Length Coding

- Counting the number of adjacent pixels with the same gray-level value
- Used primarily for binary image
- Mostly use horizontal RLC

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)

- Assign fixed-length code words to variable
- GIF,TIFF,PDF

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

- Objective fidelity criteria
- RMS Error
- RMS Signal-To-Noise Ratio
- Subjective fidelity criteria

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