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Context-based, Adaptive, Lossless Image Coding (CALIC)PowerPoint Presentation

Context-based, Adaptive, Lossless Image Coding (CALIC)

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### Context-based, Adaptive, Lossless Image Coding(CALIC)

Authors: Xiaolin Wu and Nasir Memon

Source: IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 45, NO. 4, APRIL 1997

Speaker: Guu-In Chen

date: 2000.12.14

Where to use lossless compression

- medical imaging
- remote sensing
- print spooling
- fax
- document & image archiving
- last step in lossy image compression system
……………………….

Some methods for lossless compression

- Run Length encoding
- statistical method:
- Huffman coding
- Arithmetic coding...

- dictionary-based model
- LZW: UNIX compress, GIF,V.42 bis
- PKZIP
- ARJ...

- predictive coding
- DCPM
- LJPEG
- CALIC
- JPEG-LS(LOCO-I)
- FELICS...

- wavelet transform
- S+P
…………………………………………………………

- S+P

System Overview

Raster scan original image, pixel value I

Context-based prediction,error e

grouping and predictionmodification

modified prediction ,error

Encode using arithmetic coding

W

Sharp horizontal edge

d

horizontal edge

(t+W)/2

80

week horizontal edge

(3t+W)/4

32

homogeneous

t

8

week vertical edge

(3t+N)/4

-8

vertical edge

(t+N)/2

-32

N

Sharp vertical edge

-80

Predictiondh ~gradient in horizontal direction~vertical edge

dv ~gradient in vertical direction~horizontal edge

d=dv-dh

more realistic example(inclined edge)

Prediction error

Example above,

If I=100 then e=100-75=25

p(e)

e

e

How to improve the error distribution

Context

1. texture pattern =>C={N,W,NW,NE,NN,WW,2N-NN,2W-WW}

2. Variability=>dh, dv

Influence

Group pixels

Error distribution

Previous prediction error =>

Each group has its

new prediction

why?

Context

1. texture pattern =>C={N,W,NW,NE,NN,WW,2N-NN,2W-WW}

={x0,x1,x2,x3,x4, x5, x6 , x7}

bk= 0 if xk>=

1 if xk<

α=b7b6…..b0

=75

C={100, 100, 200,100,200,200,0,0}

b0~7= 0 0 0 0 0 0 1 1

α=1100000 2

I

N

b6=1

2N-NN

NN(b4=0)

I

N (b0=1)

2N-NN(b6 must be 1)

2N-NN(b6 must be 0)

I

N (b0=0)

NN(b4=1)

What means 2N-NN,2W-WW

C={N,W,NW,NE,NN,WW,2N-NN,2W-WW}

How many cases in α

There are not (b0, b4, b6 )= (1,0,0 ) and(0,1,1)

23-2=6 cases. Same as (b1, b5, b7 ).

α has 6*6*4=144 cases not 28

Context

1. texture pattern =>C={N,W,NW,NE,NN,WW,2N-NN,2W-WW}

2. Variability=>dh, dv

Previous prediction error

△= dh+dv +2

quantize △ to [0,3]

△= 0 15 42 85

Quantization

Q(△)= 0 1 2 3

Q(△) expressed by binary number ()

for example, △=70, Q(△) =2, =102

Compound and =>C(, )

for example, =11000000

=10

C(, )=1100000010

cases in C(, ) = 144*4=576

According to different C(, ) , we group the pixels.

For each C(, ) group

mean of all e

modified prediction

modified error

Example:

I=10, 11, 13, 15, 18

= 8, 10, 13, 16, 14

e= 2, 1, 0, -1, 4

=9, 11, 14, 17, 15

=1, 0, -1, -2, 3 more closer to I

1. Balances bit rate and complexity.

2. Seems there are redundancies in C={N,W,NW,NE,NN,WW,2N-NN,2W-WW}

& △= dh+dv +2

or may be simplified.

3. Needs more understanding of Arithmetic coding.

4. Lossless or near-lossless compression can be the another fields for our laboratory.

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