1. Image compression

2. Image Compression Why? A two way event - compression and decompression
Reducing transportation times
Reducing file size
A two way event - compression and decompression

4. Compression categories
Compression = Image coding
Still-image compression
Compression of moving image

5. INTERFRAME and INTRAFRAME PROCESSING
Point to Point
Interframe Processing
Predictive Encoding
Line to Line
Intraframe
Processing

6. Image compression meters
Compress ratio =
Original image size
Compressed image size
The larger the compression ratio, the smaller the result image

7. Image compression
Compression method is not same as the image file-interchange format.
Example TIFF -file format supports several compression methods

9. Why Can We Compress? Spatial redundancy Temporal redundancy
Neighboring pixels are not independent but correlated
Temporal redundancy

10. DATA = INFORMATION + REDUNDANT DATA
Information vs Data
REDUNDANTDATA
INFORMATION
DATA = INFORMATION + REDUNDANT DATA

11. Image compression fundamentals
Same compression method is not to be used more than once.
But you can use different methods at the same time, especially different lossless methods like LZW and PKZIP

12. Image compression: symmetry

13. Color image compression
RGB - apply the same compression scheme to the three color component images
Convert the image from the RGB color space to a less redundant space, because RGB components carries a lot of same information.
RGB --> HSB, when Hue and Saturation components are well compressed

14. Color image compression
RED
GREEN
BLUE
Color image compression
HUE
BRIGHTNESS
SATURATION

15. Lossless image compression
Image can be decompressed back to original
Used when image’s future purpose of use is not known, example space exploration imagery is often studied for years following its origination

16. Run-Length Coding y
76| 5
78| 1
79| 2
80| 3
98| 2
Run-Length Codes
(Brightness | Run-length) x

17. Run-length coding
Codes the nearby pixels which has same brightness values in two values - Run-Length, RLE and brightness value
Error sensitive
Data explosion
Data errors

18. Huffman or Entropy Coding
Converting the pixel brightness values in the original image to new variable-length codes, based on their frequency of occurrence in the image
Arrange values
in descending
frequency of
occurrence
Assign Huffman
variable-length
codes
Brightness
Histogram
Huffman Code
Image Data
Raw Image
Data
Substitute
Huffman
codes
Append
code
list
0,10,0,1100
1111,11011
98,100,103,
87,86,95...
The flow of the Huffman coding operation.

19. Lossless or Lossy Compression
Lossless compression
There is no information loss, and the image can be reconstructed exactly the same as the original
Applications: Medical imagery, Archiving
Lossy compression
Information loss is tolerable
Many-to-1 mapping in compression eg. quantization
Applications: commercial distribution (DVD) and rate constrained environment where lossless methods can not provide enough compression ratio

20. Predictive Coding
Based on the assumption that pixel’s brightness can be predicted based on the brightness of the preceding pixel
Codes only the brightness value of the pixel next to each other
DPCM (Differential Pulse Code Modulation)

21. DPCM (Differential Pulse Code Modulation)

22. Block Coding Searching for repeated patterns (mostly in rows)
Pixel patterns are put in Codebook
Original image’s pixel pattern is replaced by codebook index in compressed image

23. Block Coding LZW- compression (Lempel-Ziv-Welch)
Compression ratio 2:1 - 3:1
Starting with a 256 single-pixel long codebook -> adding until it reaches its maximum length
LZW+Huffmann, where most common pixel patterns get shortest codes

24. TRANSFORM CODING • 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

25. Transform Coding
A form of lossy block coding, but it does not use codebook
Frequency domain
Frequency transformation finds the essential data in the image and coding is accurate
8*8 pixel blocks
Discrete Cosine Transform (DCT)
- Fourier -muunnetussa kuvassa kasautumia erityisesti matalissa taajuuksissa.
- Näitä yleisimpiä taajuuksia kuvataan sitten tarkoilla arvoilla ja harvinaisia taajuuksia epätarkemmin
- Siten kuvasta ei menetetä oleellista tietoa
- yleisimpien komponenttien resoluutiota voidaan myös pudottaa
- DCT on kuten FFT, mutta soveltuu paremmin pienille kuville ja 8*8 lohkoissa se on iso etu
-JPEG SPatial > frequency domain (DCT), otetaan sitten sioimmat komponentit

26. Why Do We Need International Standards?
International standardization is conducted to achieve inter-operability .
Only syntax and decoder are specified.
Encoder is not standardized and its optimization is left to the manufacturer.
Standards provide state-of-the-art technology that is developed by a group of experts in the field.
Not only solve current problems, but also anticipate the future application requirements.

27. Compression standards: JPEG
Joint Photographic Experts Group (JPEG)
One of the most important image data compression standards
Developed for highly detailed gray-scale and color images / photographs
Most commonly used as a lossy image compression method, but lossless modes exist as well
JPEG uses several cascaded compression modes
Adjustable compression scheme à number of retained frequency components can be changed to achieve different compression ratios
DCT > Remove rare frequency components > DPCM/RLE > Huffman
- Häviöttömässä JPEG:ssä ei tehdä lainkaan harvinaisten komponenttien poistoa

28. JPEG (Intraframe coding)
First generation JPEG uses DCT+Run length Huffman entropy coding.
Second generation JPEG (JPEG2000) uses wavelet transform + bit plane coding + Arithmetic entropy coding.

29. Why DCT Not DFT?
DCT is similar to DFT, but can provide a better approximation with fewer coefficients
The coefficients of DCT are real valued instead of complex valued in DFT.

30. The 64 (8 X 8) DCT Basis Functions
Each 8x8 block can be looked at as a weighted sum of these basis functions.
The process of 2D DCT is also the process of finding those weights.
This is another explanation of 2D DCT.

31. Zig-zag Scan DCT Blocks
Why? -- To group low frequency coefficients in top of vector.
Maps 8 x 8 to a 1 x 64 vector.

32. Original

33. JPEG
27:1

34. JPEG2000
27:1

35. Motion compression standards
Moving Picture Experts Group (MPEG)
Intended for the mass distribution of motion video sequences
Compression-asymmetric = compression techniques require more processing time and computing power than the decompression ones
In addition to coding techniques used with JPEG, MPEG utilizes interframe coding methods
MPEG-1 use CD-ROM and Internet
MPEG-2 use DVD and Digi-TV
MPEG-4 most advanced technology