31-10-2012, 11:30 AM
Image Compression
Image Compression.doc (Size: 61.5 KB / Downloads: 27)
Image Compression2.doc (Size: 66.5 KB / Downloads: 31)
INTRODUCTION
Every day, an enormous amount of information is stored, processed, and transmitted digitally. Companies provide business associates, investors, and potential customers with financial data annual reports, inventory, and products information on the internet. Order entry and tracking, digital- or e-government made the entire catalog of the Library of congress, the world’s largest library, electronically accessible; and cable television programming on demand is on the verge of becoming a reality. Because much of this on-line information is graphical the storage requirement are immense. Method of compressing data prior to storage are of significant practical and commercial interest.
WHAT IS IMAGE COMPRESSION?
Image compression addresses the problem of reducing the amount of data required to represent a digital image. The basis of the reduction process is the removal of redundant data. From a mathematical viewpoint, this amount to transforming a 2-D pixel array into a statistically uncorrelated data set. The transformation is applied prior to storage or transmission of the image and later it is decompressed to reconstruct the original image.
Currently, image compression is recognized as an “ enabling technology. Image compression is the natural technology for handling the increased spatial resolutions of today’s imaging sensors and evolving broadcast television standards. Furthermore, images compression plays a major role in many important and diverse applications, including televideo conferencing remote sensing (the use of satellite imagery for weather
And other earth-resource applications), document and medical imaging, facsimile transmission(FAX), and the control of remotely piloted vehicles in military space, and hazardous waste management applications.
DATA COMPRESSION:
The term data compression refers to the process of reducing the amount of data required to represent a given quantity of information. Data are the means by which information is conveyed. Various amount of data may be used to represent the same amount of information. If the two individuals use a different number of words to tell the same basic story, two different versions of the story are created, and at least one includes nonessential data. It is thus said to contain data redundancy.
DATA REDUNDANCY
Data redundancy is a central issue in digital image compression. If n1 and n2 denote the number of the information-carrying units in two data sets that represent the same information, the relative data redundancy RD of the first data set ( the one characterized by n1) can be defined as
CODING REDUNDANCY
Assigning fewer bits to the more probable gray levels than to the less probable ones achieves data compression. This process commonly is referred to as variable-length coding. In this case, the underlying basis for the coding redundancy is that images are typically composed of objects that have a regular and somewhat predictable morphology(shape) and reflectance, and are generally sampled so that the object being depicted are much larger than the picture elements. The natural consequence is that, in most images, certain gray levels are more probable than others ( that is the histograms of the most images are not uniform). A natural binary coding of their gray levels assigns the same number of bits to both the most and least probable values, thus failing to minimize and resulting in coding redundancy.
INTERPIXEL REDUNDANCY
The gray levels in images are not equally probable, variable-length coding can be used to reduce the coding redundancy that would result from a straight or natural binary encoding of their pixels. The codes used to represents the gray levels of each image have nothing to do with the correlation between pixels. These correlations result from the structural or geometric relationships between the object in the image.
Because the value of any given pixel can be reasonably predicted from the value of its neighbors, the information carried by individual pixels is relatively small. Much of the visual contribution of a single pixel to an image is redundant; it could have been guessed on the basic of the values of its neighbors. We use the term interpixel redundancy to encompass them all.
PSYCHO VISUAL REDUNDANCY
That the brightness of a region, as perceived by the eye depends on factors other than simply the light reflected by the region. For example, intensity variations (Match bands) can be perceived in an area of constant intensity. Such phenomena result from the fact that the eye does not respond with equal sensitivity to all visual information. Certain information simply has less relative importance than other information in normal visual processing. This information is said to be psycho visually redundancy. It can be eliminated without significantly impairing the quality of image perception.
That psycho visual redundancies exist should not come as a surprise, because human perception of the information in an image normally does not involve quantitative analysis of very pixel value in the images. In the general, an observer searches for distinguishing features such as edges or textural regions and mentally combines them into recognizable groupings. The brain then correlates these grouping with prior knowledge in order to complete the image interpretation process.
COMPRESSION SYSTEM
A compression system consists of two distinct structural blocks: an encoder and a decoder. An input image f(x,y) is fed into the encoder which creates a set of symbols from the input data. After transmission over the channel, the encoded representation is fed to the decoder, where a reconstructed output image f(x,y) is generated. In general f(x,y) may or may not be an exact replica of f(x,y) if it is the system is error free or information preserving: if not some level of distortion is present in the reconstructed image.
The encoded it made up of a source encoder, noise immunity of the source encoder’s output. As would be expected the decoder includes a channel decoder followed by a source decoder. It the channel between the encoder and decoder is noise free (not prone to error) the channel encoder and decoder are omitted and the general encoder and decoder become the source encoder and decoder respectively.