8.Image Compression

8.1 Fundamentals

8.1.4 Measuring Image Information -Information theory provides the mathematical framework to answer this and related question

8.1.5 Fidelity Criteria 8.1.6 Image Compression Models 8.1.7 Image formats, Containers, and Compression Standards

8.1.1 Coding Redundancy -It is present when the codes assigned to a set of events do not take full advantage of the probabilities of the events

8.1.2 Spatial and Temporal Redundancy -The run-lengths or differences between adjacent pixels can be used. -Transformations of this type are called mappings -A mapping is said to be reversible if the pixels of the original 2-D intensity array ca be reconstructed without error from the transformed data set -Otherwise the mapping is said to be irreversible

8.1.3 Irrelevant Information -A histogram equalized version of the image makes the intensity changes visible and reveals two previously undetected region of constant
intensity one oriented vertically and the other horizontally

8.1.4 Measuring Image Information -Shannon looked at representing groups of n consecutive source symbols with a single code word

8.1.5 Fidelity Criteria - Because information is lost, a means of quantifying the nature of the loss is needed. -Two types of criteria can be used for such assessment 1. objective fidelity criteria 2. Subjective fidelity criteria

8.1.6 Image Compression Models -An image compression system is composed of two distinct functional components : an encoder and a decoder. -The encoder performs compression -The decoder performs the complementary operation of decompression.

8.1.7 Image formats, Containers, and Compression Standards -It is divided Still Image and video. -Still Image is also divided Binary and Continuous Tone -In the Binary, There are CCITT Group3, CCITT Group4, JBIG etc. -In the Continuous Tone, There are JPEG,BMP,GIF etc -In the Video MPEG-1, AVS, HDV etc.

8.2 Some Basic Compression Methods

8.2.1 Huffman Coding -When coding the symbols of an information source individually, Huffman coding yields the smallest possible number of code symbols per source symbol.

8.2.2 Golomb Coding -Inputs of this type can be optimally encoded using a family of codes that are computationally simpler than Huffman codes. -The codes themselves were first proposed for the representation of nonnegative run lengths.

8.2.3 Arithmetic Coding -Unlike the variable-length codes of the previous two sections, arithmetic coding generate nonblock codes -In arithmetic coding, thich can be traced to the work of Elias, a one -to -one correspondence between source symbols

8.2.4 LZW Coding -It is an error-free compression approach that also addresses spatial redundancies in an image -LZW assigns fixed-length code words to variable length sequences of source symbols.

8.2.5 Run-Length Coding -Compression is achieved by eliminating a simple form of spatial redundancy groups of identical intensities

8.2.6 Symbol-Based Coding -In symbol or token based coding, an image is represented as a collection of frequently occurring sub-images,called symbols.

8.2.7 Bit - Plane Coding -The run-length and symbol-based techniques of the previous sections can be applied to images with more than two intensities by processing their bit planes individually -The technique,called bit-plane coding, is based on the concept of decomposing a multilevel image into a series of binary images and compressing each binary image via one of several well-known binary compression methods

8.2.8 Block Transform Coding -In this section, we consider a compression technique that divides an image into small non-overlapping blocks of equal size and processes the blocks independently using a 2-D transform.

8.2.9 Predictive Coding -The approach is based on eliminating the redundancies of closely spaced pixels by extracting and coding only the new information in each pixel.

8.2.10 Wavelet Coding - It based on the idea that the coefficients of a transform that decorrelates the pixels of an image can be coded more efficiently than the original pixels themselves.

8.3 Digital Image Watermarking