Image processing and compression techniques
Download as PPTX, PDF3 likes3,531 views
This document discusses image processing and provides examples of its applications. It covers the following key points: 1) Image processing involves analyzing and transforming images and can be used to extract information. The Mars Exploration Rover mission used image processing to compress and send images back to operators on Earth. 2) There are three main types of image processing: image-to-image, image-to-information, and information-to-image. Color spaces and compression techniques like Huffman coding are also discussed. 3) Huffman coding assigns variable length codes to characters based on their frequency, allowing for more common characters to be encoded with fewer bits and improving compression without loss of information. It has numerous applications including in
Image processing and compression techniques
- 2. Introduction Ultimate aim Acquiring images-real time example Types Color spaces Real time processing Compression Huffman coding Conclusion bibliography
- 3. Processing or altering the existing image in the desired manner. It pertains to the analysis of the pictorial representation. The main instance of image processing is Human Eye that receives , enhances and store images at enormous rate.
- 4. Extract important features from image data By doing so, a description, understanding and interpretation can be provided by the machines. There are applications to extract information But there is no proper tool to analyze.
- 5. Scientific instruments commonly produce images to the operator rather than communicating through audible tone. Space missions always have cameras . The success of the mission can be measured by the quality of the images.
- 6. Mars Exploration Rover(MER) began in 2003 by sending 2 rovers MER-A and MER-B The image of the planet Earth from Mars is given in the next slide.
- 8. The image of the planet Earth was taken by Rover during its 529th day on the martian surface. The image is compressed and then sent to the operator for efficiency. So this is where image processing is used.
- 9. Image-to-image transformation. Image-to-information transformation. Information-to-image transformation.
- 10. Conversion from RGB to YIQ/YUV loses no information. Y, the “luminance” signal, is just the brightness of a panchromatic monochrome image displayed in black and white TV
- 11. An interactive image processing followed in many countries
- 12. Redundant information can be compressed. Identical information can be grouped. Without any change in picture quality.
- 13. Run length Encoding grouping up of redundant information and compressing it.
- 14. Compression by huffman coding. Loss in image quality. This is the first image on the internet.
- 15. Split images into color and gray scale information. Group pixel into 8x8 blocks and transform through discrete cosine transform.
- 16. Proposed by Dr. David A. Huffman in 1952 ◦ “A Method for the Construction of Minimum Redundancy Codes” Huffman coding is a form of statistical coding Not all characters occur with the same frequency. But allocated the same size. 1 char = 1 byte
- 17. Scan text to be compressed and tally the occurrences of all characters. Sort or prioritize characters based on number of occurrences in text. Build Huffman code tree based on prioritized list. Perform a traversal of tree to determine all code words. Scan text again and create new file using the Huffman codes.
- 19. Eerie eyes seen near lake. 00001011000001100111000101011011010 01111101011111100011001111110100100 101 Once the receiver receives the encoded characters , it decrypts with the help of tree. 0-move left 1-move right
- 20. Image processing is any form of signal processing. Input is an image. Output may be information or image.
- 21. Lossy compression by Dr.Zhu liu. John C. Ross. Image Processing Hand book, CRC Press. 1994 Cs102 huffman coding.
- 22. Thank you