1. 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:

2. Where to use lossless compression
medical imaging
remote sensing
print spooling
fax
document & image archiving
last step in lossy image compression system
……………………….

3. 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
…………………………………………………………

4. System Overview Raster scan original image, pixel value I
Context-based prediction ,error e
grouping and prediction modification
modified prediction ,error
Encode using arithmetic coding

5. Prediction d W 80 (t+W)/2 32 (3t+W)/4 8 t -8 (3t+N)/4 -32 (t+N)/2 -80
dh ~ gradient in horizontal direction~vertical edge
dv ~ gradient in vertical direction~horizontal edge
d=dv- dh 80 32 8 -8
-32
-80 W
(t+W)/2
(3t+W)/4 t
(3t+N)/4
(t+N)/2 N
Sharp horizontal edge
horizontal edge
week horizontal edge
homogeneous
week vertical edge
vertical edge
Sharp vertical edge d
Ideal example

6. Prediction--continued
more realistic example(inclined edge)
Prediction error
Example above,
If I=100 then e=100-75=25

7. How to improve the error distribution
p(e)
Context
1. texture pattern =>C={N,W,NW,NE,NN,WW,2N-NN,2W-WW}
2. Variability =>dh, dv
Influence
Error distribution
Group pixels
Previous prediction error =>
Each group has its
new prediction
why?

8. Grouping
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= α=

9. What means 2N-NN,2W-WW How many cases in α NN I N b6=1 2N-NN I I
C={N,W,NW,NE,NN,WW,2N-NN,2W-WW} NN I N
2N-NN
b6=1
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
NN(b4=0) I
N (b0=1)
2N-NN(b6 must be 1)
NN(b4=1) I
N (b0=0)
2N-NN(b6 must be 0)

10. Grouping--continued Context △= 0 15 42 85
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] △=
Quantization
Q(△)=
Q(△) expressed by binary number ()
for example, △=70, Q(△) =2, =102

11. Grouping--continued Compound  and  =>C(, )
for example, =
=10
C(, )=
cases in C(, ) = 144*4=576
According to different C(, ) , we group the pixels.

12. Modify prediction 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

13. Experimental result

14. comment 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.