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Image Compression

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Image Compression

อ.รัชดาพร คณาวงษ์

วิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์

มหาวิทยาลัยศิลปากรวิทยาเขตพระราชวังสนามจันทร์

- Reducing the size of image data files
- While retaining necessary information

Original Image

Compressed Image file

extracted Image file

compress

decompress

- 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

(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

(2)

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

To transmit an RGB 512X512, 24 bit image

via modem 28.2 kbaud(kilobits/second)

- 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

- Average information in an image.

- Average number of bits per pixel

- Coding Redundancy
- Interpixel Redundancy
- Psychovisual Redundancy

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

- An 8 gray-level image distribution shown in Table

- Original Image 8 possible gray level = 23

- Adjacent pixel values tend to be highly correlated

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

Compressed

File

Preprocessing

Encoding

Input

Compressed

File

Decoding

Postprocessing

Output

- Compression

- Decompression

There are 2 types of Compression

- Loseless Compression
- Lossy Compression

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

- 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

40

30

20

10

0 1 2 3

- 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

- Step 3 Add 2 smallest

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

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

Binary Image 8X8

horizontal

1

0

1

- 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

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

- Allow a loss in the actual image data
- Can not recreated exactly original image
- Commonly the achievable compression is mush more
- JPEG

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

- Subjective fidelity criteria