This presentation briefly explain various methods used to predict neighbors of observed pixel.

1. SUBMITTED BY :
NAVEEN KUMAR
M.E.(ECE), 2011(REGULAR)
ROLL NO. : 112610

2.  Data is not the same thing as information.
 Data is the means with which information is
expressed. The amount of data can be much larger
than the amount of information.
 Data that provide no relevant information =
redundant data or redundancy.
Image coding or compression has a goal to reduce the
amount of data by reducing the amount of redundancy

3.  n1 = data.
 n2 = data − redundancy (i.e., data after
compression).
 Compression ratio = CR = n1/n2
Relative redundancy = RD = 1 − 1/CR

4. CR Coding Redundancy.
IR Interpixel Redundancy.
PVR Psycho-Visual Redundancy

6. Image compression can be:
 Reversible (loss less), with no loss of information.
 A new image is identical to the original image (after
decompression).
 Reversibility is necessary in most image analysis applications.
 The compression ratio is typically 2 to 10 times.
 Examples are Huffman coding and run-length coding.
 Non reversible (lossy), with loss of some information.
 Lossy compression is often used in image communication,
video,WWW, etc.
 It is usually important that the image visually is still nice.
 The compression ratio is typically 10 to 30 times.

7.  There is often correlation between adjacent
pixels, i.e., the value of the neighbors of an
observed pixel can often be predicted from the
value of the observed pixel.
 Coding methods:
 Run-Length coding.
 Difference coding

8.  Every code word is made up of a pair (g, l) where g is the gray
level, and l is the number of pixels with that gray level (length,
or “run”).
 E.g.,
56 56 56 82 82 82 83 80
56 56 56 56 56 80 80 80
creates the run-length code (56, 3)(82, 3)(83, 1)(80, 4)(56, 5).
 The code is calculated row by row.
 Very efficient coding for binary data.
 Important to know position, and the image dimensions must
be stored with the coded image.
 Used in most fax machines.la University) Image Coding an

11. Compression Achieved
Original image requires 3 bits per pixel (in total - 8x8x3=192 bits).
Compressed image has 29 runs and needs 3+3=6 bits per
run (in total - 174 bits or 2.72 bits per pixel).

12.  f (xi ) = Xi if i = 0,
xi − xi-1 if i > 0
 E.g.,
original 56 56 56 82 82 82 83 80 80 80 80
Code f(xi ) 56 0 0 26 0 0 1 −3 0 0 0
 The code is calculated rob by row.
 Both run-length coding, and difference coding are
reversible, and can be combined with, e.g., Huffman
coding

14.  Requires no priori knowledge of pixel probability
distribution values.
 Assigns fixed length code words to variable
length sequences.
 Patented Algorithm US 4,558,302
 Included in GIF and TIFF and PDF file formats

15. 39 39 126 126
As the encoder examines image pixels,
39 39 126 126 gray level sequences (i.e., blocks) that are
not in the dictionary are assigned to a new
39 39 126 126 entry.
39 39 126 126
Dictionary Location Entry
0 0 - Is 39 in the dictionary……..Yes
1 1 - What about 39-39………….No
. .
- Then add 39-39 in entry 256
255 255
256 -
39-39
511 -

16.  A predictive coding approach.
 Each pixel value (except at the boundaries) is
predicted based on its neighbors (e.g., linear
combination) to get a predicted image.
 The difference between the original and
predicted images yields a differential or residual
image.
 i.e., has much less dynamic range of pixel values.
 The differential image is encoded using Huffman
coding.

17.  Digital Image Processing by Gonzalez &
Woods
 web.uettaxila.edu.pk/CMS/.../notes/Image
%20Compression.ppt
 hpourreza.profcms.um.ac.ir/imagesm/196/.../c
h08-compression.ppt
 discovery.bits-
pilani.ac.in/discipline/physics/.../compression-
II.ppt