chapter-8imagecompression-170804060146.pdf
- 1. Image Compression Subject: FIP (181102) Prof. Asodariya Bhavesh ECD,SSASIT, Surat
- 2. Digital Image Processing, 3rd edition by Gonzalez and Woods
- 3. Preview • Image Compression is the art and science of reducing amount data required to represent an image • Data required for two hour standard definition(SD) television movie using 720×480×24 bits pixel arrays • Answer is 224 Gbytes • To save storage space and reduce transmission time • If you have 8 megapixel camera then what is the size of one uncompressed image?
- 4. Preview • Applications in many other areas like televideo conferencing, remote sensing, document and medical imaging, and Facsimile transmission(FAX)
- 5. Fundamentals • Data compression refers to the process of reducing the amount of data required to represent a given quantity of information • Data and Information are not the same thing • Data are the means by which information is conveyed • Various amounts of data can be used to represent the same amount of information • Data contains irrelevant or repeated information called redundant data
- 6. Fundamentals • Relative data redundancy R • R = 1 – 1/C where C commonly called the compression ratio is defined as • C = b/b’ where b and b’ denote the number of bits in two representations of the same information • If C = 10,for instance, means larger representation has 10 bits of data for every 1 bit of data in the smaller representation • Corresponding relative data redundancy of the larger representation is 0.9 indicating 90% of its data is redundant
- 7. Principal types of data redundancies • 1) Coding Redundancy 2) Spatial and temporal redundancy 3) Irrelevant information • 1) Coding Redundancy: A code is a system of symbols (letters, numbers, bits) used to represent a body of information or set of events • Each piece of information or event is assigned a sequence of code symbols, called a code word • Number of symbols in each code word is its length • 2) Spatial and temporal redundancy: Pixels of most 2-D intensity arrays are correlated spatially (i.e. each pixel is similar to or dependent on neighboring pixels
- 8. Principal types of data redundancies • Information is unnecessarily replicated in the representations of the correlated pixels • In a video sequence, temporally correlated pixels also duplicate information • 3) Irrelevant information: Most 2-D intensity arrays contain information that is ignored by human visual system and/or extraneous to the intended use of the image. It is redundant in the sense that it is not used
- 9. Principal types of data redundancies
- 12. Spatial and Temporal Redundancy
- 14. Measuring Image Information • How few bits are actually needed to represent the information in an image? • Is there a minimum amount of data that is sufficient to describe an image without losing information? • Answer is given by Information Theory • I(E) = log(1/P(E) )= - log(P(E)) units of information • If the base 2 is selected, the unit of information is the bit
- 15. Measuring Image Information • Given a source of statistically independent random events from a discrete set of possible events {a1,a2,…..,aj} with associated probabilities {P(a1), P(a2),……,P(aj)}, the average information per source output, called the entropy of the source, is • aj is called the source symbols • Because they are statistically independent, the source itself is called a zero-memory source
- 16. Measuring Image Information • H for previous example (first image) is 1.6614 bits/pixel • H for second image is 8 bits/pixel • H for third image is 1.566 bits/pixel • Shannon’s first theorem
- 18. Some Basic Compression Methods • Huffman Coding • Most popular techniques for removing coding redundancy
- 19. Huffman Coding
- 20. Some Basic Compression Methods • Arithmetic Coding • Generates nonblock codes and it is used to remove coding redundacy • One to one correspondence between source symbols and code words does not exist • Entire sequence of source symbols is assigned a single arithmetic code word • Number of symbols in the message increases, the interval used to represent it becomes smaller and the number of information units required to represent the interval becomes larger
- 22. Arithmetic Coding • (Higher value-lower value)*Prob + lower value
- 23. Arithmetic Coding • Three decimal digits are used to represent the five symbol message • 0.6 decimal digits per source symbol • Entropy is 0.58 decimal digit per source symbol • Length of the sequence being coded increases, the resulting arithmetic code approaches the bound established by shannon’s first theorem • Two disadvantage 1) the addition of the end of message indicator that is needed to separate one message from another ; 2) the use of finite precision arithmetic
- 24. LZW Coding • Addresses spatial redundancies in an image • The technique, called Lempel-Ziv-Welch (LZW) coding, assigns fixed length code words to variable length sequences of source symbols • Probabilities are not required • It was protected under a United States patent, LZW compression has been integrated into a variety of mainstream imaging file formats, including GIF, TIFF, and PDF. The PNG format was created to get around LZW licensing requirements
- 25. LZW Coding
- 27. Run Length Coding • Images with repeating intensities along their rows(columns) can often be compressed by representing runs of identical intensities as run-length pairs, where each run-length pair specifies the start of a new intensity and the number of consecutive pixels that have that intensity
- 28. Run Length Coding • RLE was developed in 1950s and used in FAX coding • Compression is achieved by eliminating a simple form of spatial redundancy-group of identical intensities • When there are few(or no) runs of identical pixels, run-length encoding results in data expansion • BMP file format uses a form of run-length encoding in which image data is represented in two different modes; encoded and absolute • Either mode can occur anywhere in the image • Encoded mode, a two byte RLE representation is used. The first byte specifies the number of consecutive pixels that have the color index contained in the second byte. The 8-bit color index selects the run’s intensity from a table of 256 possible intensities
- 29. Run Length Coding • Absolute mode, the first byte is 0 and the second byte signals one of four possible conditions as shown in table • When the second byte is 0 or 1, the end of a line or the end of the image has been reached • If it is 2, the next two bytes contain unsigned horizontal and vertical offsets to a new spatial position (and pixel) in the image Second Byte Value Conditions 0 End of line 1 End of image 2 Move to a new position 3-255 Specify pixels individually
- 30. Run Length Coding • Effective when compressing binary images • Additional compression can be achieved by variable length coding the run lengths themselves • The approximate run-length entropy of the image is then HRL = H0 + H1 /L0 + L1 • Where the variables L0 and L1 denote the average values of black and white run lengths, respectively
- 31. Types of File Formats • Simplest way of storing image data is by using a 2D array of pixel intensities. This is referred to as a Bitmap. • Another way of encoding the images is to use vector graphics where the image is stored as a collection of vectors. • Popular file formats are listed below: • 1) GIF(Graphics Interchange Format) 2) JPEG(Joint Photographic Experts Group) 3) PNG (Portable Network Group) 4) DICOM(Digital Imaging and COMmunication) 5) SVG (Vector Graphics file format) 6) TIFF (Tagged Image File Format
- 32. Types of File Formats • 1) GIF: Uses lossless compression LZW technique • Quality of image is very high • It supports 256 colors (8-bit) • File is smaller in size, has good compression, and is good in displaying flat color areas • Also supports animation • Can store multiple images and using timing information can build animations where multiple static images play continuously, creating the illusion of motion • 2) JPEG : Used for storing continuous tone images • Provides lossy and lossless compression
- 33. Types of File Formats • Used DCT and DWT technique for compression • Common format for storing and transmitting photographic images on the World Wide Web • 3) PNG : Specially designed for the Web • Supports grey scale or RGB images • Designed for transmitting images on the Internet • Supports transparency and interlacing • One useful feature of PNG is its built-in text capabilities for image indexing, allowing storage of text within the file itself
- 34. Types of File Formats • 4) DICOM : Popular format in medical imaging • Contains image data and also metadata such as patient details, equipment, and acquisition details • Provides many communication standards • 5) SVG : It is a vector graphics file format that enables 2D images to be displayed on the web • Scalable to the size of the viewing window and adjust in size and resolution according to the window in which they are displayed • 6) TIFF : A flexible file format supporting a variety of image compression standards, including JPEG, JPEG-LS, JPEG- 2000, and others