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INTRODUCTION
Image processing is one of the most powerful technologies that will shape science and engineering in the twenty first century. In the broadest sense, image processing is any form of information processing for which both the input and output are images, such as photographs or frames of video. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it.
SIGNAL PROCESSING
Signal processing is the processing, amplification and interpretation of signals and deals with the analysis and manipulation of signals.
SOLUTION METHODS
A few decades ago, image processing was done largely in the analog domain, chiefly by optical devices. These optical methods are still essential to applications such as holography because they are inherently parallel; however, due to the significant increase in computer speed, these techniques are increasingly being replaced by digital image processing methods.
Digital image processing techniques are generally more versatile, reliable, and accurate; they have the additional benefit of being easier to implement than their analog counterpart. Today, hardware solutions are commonly used in video processing systems. However, commercial image processing tasks are more commonly done by software running on conventional personal computers.
IMAGE RESOLUTION
Image resolution describes the detail an image holds. The term applies equally to digital images, film images, and other types of images. Higher resolution means more image detail.
Image resolution can be measured in various ways. Basically, resolution quantifies how close lines can be to each other and still be visibly resolved. Resolution units can be tied to physical sizes or to the overall size of a picture. Furthermore, line pairs are often used instead of lines. A line pair is a pair of adjacent dark and light lines, while lines count both dark lines and light lines.
PIXEL RESOLUTION
The term resolution is often used as a pixel count in digital imaging None of these pixel resolutions are true resolutions, but they are widely referred to as such; they serve as upper bounds on image resolution.
Below is an illustration of how the same image might appear at different pixel resolutions, if the pixels were poorly rendered as sharp squares (normally, a smooth image reconstruction from pixels would be preferred, but for illustration of pixels, the sharp squares make the point better?)
EDGE DETECTION
The goal of edge detection is to mark the points in a digital image at which the luminous intensity changes sharply. Edge detection is a research field within image processing and computer vision, in particular within the area of feature extraction. Edge detection of an image reduces significantly the amount of data and filters out information that may be regarded as less relevant, preserving the important structural properties of an image.
TYPICAL PROBLEMS
The red, green, and blue color channels of a photograph. The fourth image is a composite.
• Geometric transformations such as enlargement, reduction, and rotation
• Color corrections such as brightness and contrast adjustments, quantization, or conversion to a different color space
• Registration (or alignment) of two or more images
• Combination of two or more images, e.g. into an average, blend, difference, or image composite
• Interpolation, demosaicing, and recovery of a full image from a RAW image format.
• Segmentation of the image into regions
• Image editing and digital retouching
• Extending dynamic range by combining differently exposed images.
and many more.
Besides static two-dimensional images, the field also covers the processing of time-varying signals such as video and the output of tomographic equipment. Some techniques, such as morphological image processing, are specific to binary or grayscale images.
IMAGE COMPRESSION
The objective of compression is to reduce the data volume and achieve reproduction of the original data without any perceived loss in data quality. The neighbouring pixels are correlated and therefore contain redundant information in most images. The foremost task then is to find less correlated representation of the image. Two fundamental concepts of compression are redundancy and irrelevancy reduction. Reduction is a characteristic related to the factors such as predictability, randomness and smoothness in the data. Redundancy reduction aims at removing duplication from the image while irrelevancy reduction omits parts of the signal that will not be noticed by the signal receiver. In general, three types of redundancy can be identified: spatial redundancy that exists between adjacent frames in a sequence of images. Image compression aims removing the spatial and spectral redundancies as much as possible.
Digital imaging depends have been continuously going up both due to size of the image and its resolution. Storage of picture data has become a growing need in any application. A simple gray scale image of 512x512 pixels will need a storage array of 256 bytes assuming that the pixel information is 8 bit wide (0-255 representing white to black on a 256 discrete scale)
A 35mm slide if digitized with a solution of about 12 microns will need 18 megabytes of data storage. In general, picture frame data compression when can be separated into
• Lossy compression
• Lossless compression
LOSSY COMPRESSION
In lossy compression schemes, the compressed image contains degradation relative to the original image while the compression achieved much higher compression than the lossless compression because it completely discards redundant information. Lossy encoding is base don the concept of compromising the accuracy of the reconstructed image in exchange for increased compression. If the resulting distortion (which may or may not be visually apparent) can be tolerated, the increase in the compression can be significant.
Lossy image compression is useful in applications such as broad cast television, video conferencing and facsimile transmission, in which a certain amount of error is an acceptable tradeoff for increased compression performances. Lossy compression usually prohibited for legal reasons
LOSSLESS COMPRESSION
In lossless compression schemes, the compressed image is numerically identical to the original image while the compression can only achieve a modest amount. In numerous applications error free compressions is the only acceptable means of data reductions. The need for error free compression is motivated by the intended use or nature of the image under consideration. They normally provide compression ratios of 2-10. Moreover they are equally applicable to both binary and gray scale images. This technique generally is composed of two relatively independent operations:
(1) Devising an alternative representation of the image in which its interpixel redundancies are reduced.
(2) Coding the representation to eliminate coding redundancies.