Final Ppt Finally

7/28/2019 Final Ppt Finally

1/15

Image Compression

By:-

Sagar Thakur

Harsha Tolani

1 of 15


2/15

What is Image Compression?

A technique used to reduce the volume ofinformation to be transmitted about an image.

Height 1 Unit M=Number of Samples

D=Spacing Between Samples

M

Width 1

Unit

M

1/2

D

2 of 15


3/15

Why is Image Compression required?

The objective ofimage compression is toreduce irrelevance and redundancy of the imagedata in order to be able to store or transmit data in

an efficient form.

Requirements may outstrip the anticipatedincrease of storage space and bandwidth.

3 of 15
http://en.wikipedia.org/wiki/Data_transmissionhttp://en.wikipedia.org/wiki/Data_transmission


4/15

Image Processing in MATLAB

The core MATLAB package comes with severalrudimentary functions that can be used to load,save, and perform custom functions on images.

The image processing toolbox allows suchmanipulations as:

Direct visualization of images in MATLAB

Color space conversions Object grouping and data collection

Image arithmetic and many others.

4 of 15


5/15

Types of Image Compression

Lossy Compression-"Lossy" compression is a dataencodingmethod that compresses data by discarding(losing) some of it. The procedure aims to minimize theamount of data that needs to be held, handled, and/or

transmitted by a computer. Lossless Compression- " Lossless data "

compression is a class ofdatacompressionalgorithms that allows the exact original datato be reconstructed from the compressed data. The

term lossless is in contrast to lossy data compression,which only allows an approximation of the original datato be reconstructed, in exchange for better compressionrates.

5 of 15
http://en.wikipedia.org/wiki/Data_compressionhttp://en.wikipedia.org/wiki/Data_compressionhttp://en.wikipedia.org/wiki/Data_compressionhttp://en.wikipedia.org/wiki/Data_compressionhttp://en.wikipedia.org/wiki/Algorithmhttp://en.wikipedia.org/wiki/Lossy_data_compressionhttp://en.wikipedia.org/wiki/Bit_ratehttp://en.wikipedia.org/wiki/Bit_ratehttp://en.wikipedia.org/wiki/Bit_ratehttp://en.wikipedia.org/wiki/Bit_ratehttp://en.wikipedia.org/wiki/Lossy_data_compressionhttp://en.wikipedia.org/wiki/Algorithmhttp://en.wikipedia.org/wiki/Data_compressionhttp://en.wikipedia.org/wiki/Data_compressionhttp://en.wikipedia.org/wiki/Data_compressionhttp://en.wikipedia.org/wiki/Data_compression


6/15

Uses of Lossy & Lossless Compression

Lossy compression is most commonly used tocompress multimedia data (audio,video, and still images),especially in applications such as streaming media and internettelephony.

Lossless compression is used in cases where it is importantthat the original and the decompressed data be identical, orwhere deviations from the original data could be deleterious.

Typical examples are executable programs, text documents,and source code. Some image file formats, like PNG or GIF,

use only lossless compression, while otherslikeTIFF and MNG may use either lossless or lossy methods.

6 of 15
http://en.wikipedia.org/wiki/Multimediahttp://en.wikipedia.org/wiki/Sound_recording_and_reproductionhttp://en.wikipedia.org/wiki/Videohttp://en.wikipedia.org/wiki/Imagehttp://en.wikipedia.org/wiki/Streaming_mediahttp://en.wikipedia.org/wiki/VOIPhttp://en.wikipedia.org/wiki/VOIPhttp://en.wikipedia.org/wiki/Portable_Network_Graphicshttp://en.wikipedia.org/wiki/Graphics_Interchange_Formathttp://en.wikipedia.org/wiki/TIFFhttp://en.wikipedia.org/wiki/Multiple-image_Network_Graphicshttp://en.wikipedia.org/wiki/Multiple-image_Network_Graphicshttp://en.wikipedia.org/wiki/TIFFhttp://en.wikipedia.org/wiki/Graphics_Interchange_Formathttp://en.wikipedia.org/wiki/Portable_Network_Graphicshttp://en.wikipedia.org/wiki/VOIPhttp://en.wikipedia.org/wiki/VOIPhttp://en.wikipedia.org/wiki/Streaming_mediahttp://en.wikipedia.org/wiki/Imagehttp://en.wikipedia.org/wiki/Videohttp://en.wikipedia.org/wiki/Sound_recording_and_reproductionhttp://en.wikipedia.org/wiki/Multimedia


7/15

Example of Lossy Compression

7 of 15


8/15

Lossy Techniques

Transformation coding

Vector quantization

Fractal coding Block Truncation coding

Sub band coding

8 of 15


9/15

Lossless Techniques

Run length encoding

Huffman encoding

LZW codingArea coding

Sub band coding

9 of 15


10/15

Huffman Encoding

Huffman code procedure is based on the twoobservations. More frequently occurred symbols will have shorter

code words than symbol that occur less frequently.

The two symbols that occur least frequently will havethe same length.

The Huffman code is designed by merging the lowest

probable symbols and this process is repeated until onlytwo probabilities of two compound symbols are left andthus a code tree is generated and Huffman codes areobtained from labeling of the code tree.

10 of 15


11/15

Huffman Coding and Decoding Algorithm

Step1- Read the image on to the workspace of the mat lab.

Step2- Convert the given colour image into grey level image.

Step3- Call a function which will find the symbols (i.e. pixel value which

is non-repeated).

Step4- Call a function which will calculate the probability of eachsymbol.

Step5- Probability of symbols are arranged in decreasing order and lowerprobabilities are merged and this step is continued until only twoprobabilities are left and codes are assigned according to rule that :the

highest probable symbol will have a shorter length code. 11 of 15


12/15

Step6- Further Huffman encoding is performed i.e. mapping of thecode words to the corresponding symbols will result in a compresseddata.

Step7- The original image is reconstructed i.e. decompression is doneby using Huffman decoding.

Step8- Generate a tree equivalent to the encoding tree.

Step9- Read input character wise and left to the table II until lastelement is reached in the table II.

Step10-Output the character encode in the leaf and return to the root,and continue the step9 until all the codes of corresponding symbolsare known.

12 of 15


13/15

Static Code tree

13 of 15


14/15

References

Gonzalez and Woods, Digital image processing, 2nd edition,Prentice Hall, 2002.

Wikipedia Encyclopedia, Feb 2011.

Little, Jack, and Cleve Moler. MathWorks India. 07 Apr. 2012.

14 of 15
http://www.mathworks.in/http://www.mathworks.in/


15/15

THANK YOU!

15 of 15

Final Ppt Finally

Documents