Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- Image compression by Bassam Kanber 10028 views
- Image compression by partha pratim deb 15544 views
- M.Tech project on Haar wavelet base... by Veerendra B R Rev... 1151 views
- Image compression by Visvesvaraya Nati... 82267 views
- Introduction to wavelets and wavele... by VirginiaAttwood547 145 views
- Image Compression Comparison Using ... by Jason Li 3751 views

2,100 views

Published on

Published in:
Data & Analytics

No Downloads

Total views

2,100

On SlideShare

0

From Embeds

0

Number of Embeds

10

Shares

0

Downloads

1

Comments

4

Likes

8

No notes for slide

- 1. IMAGE COMPRESSION By Garima Shakya
- 2. ● The objective of image compression is to reduce irrelevance and redundancy of the image data in order to be able to store or transmit data in an efficient form. ● Sometimes the given data contains data which has no relevant information,or restates/repeats the known information: Data redundancy. Image=Information+redundant data Need of compression...
- 3. What are the data Redundancies used in image compression??
- 4. Data redundancies: There are three main data redundancies used in image compression: ● Coding redundancy: The uncompressed image usually is coded with each pixel by a fixed length. ➔ Using some variable length code schemes such as Huffman coding and arithmetic coding may produce compression. ● Interpixel redundancy: Spatial redundancy,it exploit the fact that an image very often contains strongly correlated pixels, large regions whose pixel values are the same or almost the same.
- 5. ● Psychovisual redundancy: Human eye does not respond with equal sensitivity to all incoming visual information. ➔ Some piece of information are more important than others. ➔ Removing this type of redundancy is a lossy process and the lost information can not be recovered.
- 6. Types of compression: Lossless: ● In lossless data compression, the integrity of the data is preserved,i.e. no part of the data is lost in the process. ● Lossless compression methods are used when we cannot afford to lose any data. Lossy: ● Lossy compression can achieve a high compression ratio, since it allows some acceptable degradation. Yet it cannot completely recover the original data
- 7. Lossless Compression: Two-step algorithms: 1. Transforms the original image to some other format in which the inter-pixel redundancy is reduced. 2. Use an entropy encoder to remove the coding redundancy. The lossless decompressor is a perfect inverse process of the lossless compressor.
- 8. Lossless compression ● Huffman Coding ● Run-length Coding ● Arithmetic Coding ● Lempel-Ziv Coding
- 9. Lossless compression ● Huffman Coding ● Run-length Coding ● Lempel-Ziv Coding ● Arithmetic Coding
- 10. Huffman Coding: Huffman coding assigns shorter codes to symbols that occur more frequently and longer codes to those that occur less frequently. For example, Character A B C D E Frequency 17 12 12 27 32
- 11. Lossless compression ● Huffman Coding ● Run-length Coding ● Arithmetic Coding ● Lempel-Ziv Coding
- 12. Run-Length Coding ● It can be used to compress data made of any combination of symbols. ● It does not need to know the frequency of occurrence of symbols. ● The method is to replace consecutive repeating occurrences of a symbol by one occurrence of the symbol followed by the number of occurrences.
- 13. The method can be even more efficient if the data uses only two symbols (for example 0 and 1) in its bit pattern and one symbol is more frequent than the other.
- 14. Lossless compression ● Huffman Coding ● Run-length Coding ● Arithmetic Coding ● Lempel-Ziv Coding
- 15. Arithmetic Coding A sequence of source symbols is assigned to a sub-interval in [0,1) which can be represented by an arithmetic code. For example, message: a1a2 a3 a3 a4 1) Start with interval [0, 1) : Source symbol Probability a1 0.2 a2 0.2 a3 0.4 a4 0.2 0 1
- 16. 2) Subdivide [0, 1) based on the probabilities of symbols: Source Symbol Probability Initial Subinterval a1 0.2 [0.0, 0.2) a2 0.2 [0.2, 0.4) a3 0.4 [0.4, 0.8) a4 0.2 [0.8, 1.0) 0 1 0.2 0.2 0.4 0.2 Initial Subinterval [0.0,0.2) [0.2,0.4) [0.4,0.8) [0.8,1.0)
- 17. [0.06752, 0.0688) or 0.068 Encode a1 a2 a3 a3 a4 (must be inside sub-interval)
- 18. Lossless compression ● Huffman Coding ● Run-length Coding ● Arithmetic Coding ● Lempel-Ziv Coding
- 19. Lempel-Ziv Coding ● It is an example of a category of algorithms called dictionary-based encoding . ● The idea is to create a dictionary (a table) of strings used during the communication session. ● If both the sender and the receiver have a copy of the dictionary, then previously-encountered strings can be substituted by their index in the dictionary to reduce the amount of information transmitted.
- 20. Compression:
- 21. Decompression:
- 22. References: ● https://en.wikipedia.org/wiki/Data_compression ● https://en.wikipedia.org/wiki/Lossless_compression ● https://en.wikipedia.org/wiki/lossy_compression
- 23. Thank You!

No public clipboards found for this slide

Login to see the comments