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BY A. Lecture Karrar DH. Mohammed

Introduction in fuzzy image compression. BY A. Lecture Karrar DH. Mohammed. History of image processing. In the 1970s, digital image processing proliferated, when cheaper computers and dedicated hardware became available.

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BY A. Lecture Karrar DH. Mohammed

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  1. Introduction in fuzzy image compression BY A. Lecture Karrar DH. Mohammed

  2. History of image processing In the 1970s, digital image processing proliferated, when cheaper computers and dedicated hardware became available. Images could then be processed in real time, for some dedicated problems such as television standards conversion. As general-purpose computers became faster, they started to take over the role of dedicated hardware

  3. History of image processing for all but the most specialized and compute-intensive operations. With the fast computers and signal processors available in the 2000s, digital image processing has become the Most common form of image processing, and is generally used because it is not only the most versatile method , but also the cheapest.

  4. What is an Image • An image f (x, y) is 2-dimensional light intensity function ,where f measures brightness at position (x, y). • 2. A digital image is a representation of an image by a 2-D array of discrete samples. • 3. The amplitude of each sample is represented by a finite number of bits. • 4. Each element of the array is called a pixel.

  5. Terminology Images: An image is a two-dimensional signal whose intensity at any point is a function of two spatial variables. Examples are photographs, still video images, radar and sonar signals, chest and dental X-rays.

  6. Terminology An image sequence such as that seen in a television is a three dimensional signal for which the image intensity at any point is a function of three variables: two spatial variables and time.

  7. Terminology 1. Digital image processing is a term used to describe the manipulation of image data by a computer. 2. The process of transforming an image to a set of numbers, which a computer can utilized, is called digitization. 3. Digitization is to divide an image up into several picture elements called pixels. A pixel is the smallest resolvable unit of an image which the computer handles.

  8. 4. The value of a pixel is referred to as its gray level and can be thought of as the intensity or brightness (or darkness) of the pixel. 5. The number of different gray-levels a pixel can have varies from system to system, and is determined by the hardware that produces or displays the image.

  9. Why do we process images Images (and videos) are every where .This includes different imaging modalities such as visual, X-ray, ultrasound, etc. Multimedia information will be the wave of the future. Diverse applications in astronomy, biology, geology, geography, medicine, law enforcement, defense, Industrial inspection, require processing of images.

  10. Image compression The goal of image compression is to reduce the amount of data required to represent a digital image. Important for reducing storage requirements and improving transmissionrates.

  11. Why Compress? To reduce the volume of data to be transmitted (text, fax, images) To reduce the bandwidth required for transmission and to reduce storage requirements (speech, audio, video)

  12. Compression • - How is compression possible? • Redundancy in digital audio, image, and video data • - Properties of human perception • Digital audio is a series of sample values; image is a rectangular array of pixel values; video is a sequence of images played out at a certain rate.

  13. Human Perception Factors Compressed version of digital audio, image, video need not represent the original information exactly Perception sensitivities are different for different signal patterns Human eye is less sensitive to the higher spatial frequency components than the lower frequencies (transform coding)

  14. Classification • -- Lossless compression • - lossless compression for legal and medical documents, computer programs • - exploit only data redundancy. • -- Lossy compression • - digital audio, image, video where some errors or loss can be tolerated. • - exploit both data redundancy and human perception properties.

  15. Data vs Information Data and information are not the same terms! Datais the means by which information is conveyed. Data compression aims to reduce the amount of data required to represent a given quantity of information while preserving as much information as possible.

  16. Data vs Information • The same amount of information can be represented by various amount of data, e.g.: Ex1: Your wife, Helen, will meet you at Logan Airport in Boston at 5 minutes past 6:00 pm tomorrow night Your wife will meet you at Logan Airport at 5 minutes past 6:00 pm tomorrow night Helen will meet you at Logan at 6:00 pm tomorrow night Ex2: Ex3:

  17. What does Fuzzy Image Processing mean Fuzzy image processing is not a unique theory. It is a collection of different fuzzy approaches to image processing. Nevertheless, the following definition can be regraded as an attempt to determine the boundaries: Fuzzy image processing is the collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved Fuzzy image processing has three main stages: image fuzzification, modification of membership values, and, if necessary, image defuzzification see figure below

  18. The general structure of fuzzy image processing .

  19. What does Fuzzy Image Processing mean The fuzzification and defuzzification steps are due to the fact that we do not possess fuzzy hardware. Therefore, the coding of image data (fuzzification) and decoding of the results (defuzzification) are steps that make possible to process images with fuzzy techniques. The main power of fuzzy image processing is in the middle step (modification of membership values, see Fig.2). After the image data are transforemd from gray-level plane to the membership plane (fuzzification), appropriate fuzzy techniques modify the membership values. This can be a fuzzy clustering, a fuzzy rule-based approach, a fuzzy integration approach, a fuzzy algebra approach and so on.

  20. Steps of fuzzy image processing.

  21. Thanks for being so patient Thanks for being so patient

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