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

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Where to use lossless compression

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

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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
• SP

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System Overview
Raster scan original image, pixel value I
Context-based prediction,error e
grouping and predictionmodification modified
prediction ,error
Encode using arithmetic coding
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Prediction
dh gradient in horizontal directionvertical
edge dv gradient in vertical directionhorizonta
l edge ddv- dh
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Prediction--continued
more realistic example(inclined edge)
Prediction error
Example above, If I100 then e100-7525
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How to improve the error distribution
Context 1. texture pattern gtCN,W,NW,NE,NN,WW,2N
-NN,2W-WW 2. Variability gtdh, dv
Influence
Error distribution
Group pixels
Previous prediction error gt
Each group has its new prediction
why?
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Grouping
Context 1. texture pattern gtCN,W,NW,NE,NN,WW,2N
-NN,2W-WW x0,x1,x2,x3,x4, x5, x6 , x7
bk 0 if xkgt 1 if xklt ab7b6..b0
75 C100, 100, 200,100,200,200,0,0 b07
0 0 0 0 0 0 1 1 a1100000 2
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What means 2N-NN,2W-WW
CN,W,NW,NE,NN,WW,2N-NN,2W-WW
How many cases in a
There are not (b0, b4, b6 ) (1,0,0 )
and(0,1,1) 23-26 cases. Same as (b1, b5, b7 ).
a has 664144 cases not 28
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Grouping--continued
Context 1. texture pattern gtCN,W,NW,NE,NN,WW,2N
-NN,2W-WW 2. Variability gtdh, dv
Previous prediction error
? dhdv 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
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Grouping--continued
Compound ? and ? gtC(?, ?) for example,
?11000000 ?10
C(?, ?)1100000010
cases in C(?, ?) 1444576
According to different C(?, ?) , we group the
pixels.
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Modify prediction
For each C(?, ?) group mean of all e modified
prediction modified error
Example I10, 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
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Experimental result
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comment
1. Balances bit rate and complexity. 2. Seems
there are redundancies in CN,W,NW,NE,NN,WW,2N-N
N,2W-WW ? dhdv 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.