Error free compression and lossy compression

1. S.Nandhini
II-M Sc(CS&IT),
Nadar Saraswathi College of Arts and Science,
Theni.

2. ERROR FREE COMPRESSION
 Error free compression is the only acceptable means of data
reduction.
 One such application is the archival of medical or business
documents.
 Need for error free compression is motivated by the intended
use or nature of the images under consideration.

3. There types in error free compression
 Variable-length coding
 Huffman coding
 Arithmetic coding
 Variable-length coding
 Error-free image compression is to reduce
only coding redundancy.
 coding redundancy normally is present in
any natural binary encoding of the gray level in an image.

4. HUFFMAN CODING
 Coding the symbol of an information source individually.
 Huffman coding yields the smallest possible number of
code symbols per sources symbol.
 The constraint that the source symbol be coded one at a
time.

6. ARITHMETIC CODING
 Arithmetic coding generates non-block codes.
 A one-to-one correspondence between source symbols and
code words does not exist
 Sequence of source symbols is assigned a single arithmetic
code word.

8. LOSSY COMPRESSION
 Lossy compression encoding is based on the concept of
compromising the accuracy
 The reconstructed image in exchange for increased
compression.
 The resulting distortion can be tolerated
 The increase in compression can be significant.

9. LOSSY PREDICTIVE CODING
 Add a quantizer to the model introduced examine the
resulting trade-off between reconstruction accuracy and
compression performance.
 Lossy predictive coding model
 A)encoder
 B)decoder

10. ENCODER AND DECODER

11. DELTA MODULATION
 Delta modulation(DM) is a simple but well-known form of
lossy predictive coding in which the predictor and quantizer
are defined

12. DELTA MODULATION

14. TRANSFORM CODING
 Transform coding a reversible, linear transform is used to
map the image into a set of transform coefficients.
 Which are then quantized and coded.

15. TRANSFORM CODING

16. Performs four relatively straightforward operations
 DECOMPOSITION
 TRANSFORMATION
 QUANTIZATION
 CODING
An NXN input of image first is subdivided into subimages
of size nXn.
Which are then transformed to generate(N/n)2 sub image
transform arrays.

17. THANK YOU