Quantization
Quantization refers to the process of approximating the continuous set of values in the image data with a finite (preferably small) set of values. The input to a quantizer is the original data, and the output is always one among a finite number of levels. The quantizer is a function whose set of output values are discrete, and usually finite. Obviously, this is a process of approximation, and a good quantizer is one which represents the original signal with minimum loss or distortion.
A quantizer simply reduces the number of bits needed to store the transformed coefficients by reducing the precision of those values. Since this is a many-to-one mapping, it is a lossy process and is the main source of compression in an encoder. Quantization can be performed on each individual coefficient, which is known as Scalar Quantization (SQ).
There are two types of quantization - Scalar Quantization and Vector Quantization. In scalar quantization, each input symbol is treated separately in producing the output, while in vector quantization the input symbols are clubbed together in groups called vectors, and processed to give the output. This clubbing of data and treating them as a single unit increases the optimality of the vector quantizer, but at the cost of increased computational complexity.
Coefficients that corresponds to smooth parts of data become small. (Indeed, their difference, and therefore their associated wavelet coefficient, will be zero, or very close to it). So we can throw away these coefficients without significantly distorting the image. We can then encode the remaining coefficients and transmit them along with the overall average value.
Discrete Wavelet Transform (DWT):
A discrete wavelet transformation apply on an image consists of four frequency bands as given in figure (4.1). The top- left corner of the transformed image is the LL band of original image, low-frequency coefficients. The bottom-right corner "HH" contains residual diagonal frequencies. The main reason for use DWT, for reduce an image dimensions, and the high-frequencies coefficients are ignored (i.e. not used in this work), this will effects on the image quality, while this process increasing compression ratio.
Figure (4.1): Original Image After Discrete Wavelet Transform Application
JPEG Technique:
JPEG is a lossy image compression method for color or grayscale images. An important feature of JPEG is its use the quality parameter "Quality", allowing the user to adjust the amount of the data lost over a very wide range. In this work we apply this technique on the sub-image LL, and the image quality problem are solved in the (4.
Tanaka, K., Saito, H. A., Fukada, Y., & Moriya, M. (1991). Coding vidual images of objects
After compression, the structure data, audio and video must be multiplexed. A number of compressed TV signals are combined by a multiplexer and put unto a shared transition medium. This is done by one of the two possible kinds of multiplexers that result in either a transport or a program stream, which is suited for secure transmission paths since it can contain large amounts of information. In addition multiplexing can be done using various methods. Time division multiplexing allocates a distinct time interval for each channel in a set; with the help of synchronization and a fixed interval order the channels take turns using the common line.
the mean value and the standard deviation, to represent the global characteristics of the image, and the image bitmap is used to represent the local characteristics of the image for increasing the accuracy of the retrieval system. Aptoula et al. [8] presen...
Image quality assessment is another step in image processing in which statistical parameters are used to measure the quality of the processed image in reference to the raw image or the original image. We shall discuss that later in Chapter 5.
In many applications of image processing, the gray levels of pixels belonging to the object or foreground are quite different from the gray levels of the pixels belonging to the background. Thresholding becomes then a simple tool to separate foreground from the background. Examples of thresholding applications are document image analysis where the goal is to extract printed characters logos, graphical map processing where lines, legends, characters are to be found, quality inspection of materials etc [3].The output of the thresholding operation is a binary image whose gray level of 0 (black) will indicate ...
Vector is the complete opposite type of graphic compared to bitmaps. Vectors work by having mathematical equations to work out what way the animation is going rather than pixels. This means that they be made as larger or as smaller as possible without losing any quality.
...ignal before preceding them to digital signal processors and multi-controller. An analogue to digital converter is used to transform analogue signal into digital signals.
I have decided to compare Raster (bitmap) and Vector graphics & their uses in modern digital multimedia.
There are a great number of applications for Digital Signal Processing and in order to better understand why DSP has such a large impact on multiple aspects of society, it helps to better understand the wide variety of applications it can be used for. Here we will briefly look into the following applications of Digital Signal Processing and their uses; speech and audio compression, communications, biomedical signal processing and applications in the automobile manufacturing industry. Li Tan [1] goes into detail with each of these applications in his book, Digital Signal Processing, and explains how each are used on a daily basis.
In the Using the pulse modulation technique, the analog signal is converted into the digital signal. The process of quantization and companding of a signal is carried on the spreadsheets. To attain a clear signal to quantization noise ratio, the number of samples should be increased. By increasing the sampling depth, the quantization error can be minimized. By companding process, the quantization noise and distortion levels can be minimized. Companding improves response for low amplitude signals.
The 3-D DWT can be considered as a combination of three one dimensional DWT in the x, y and z directions, as shown in Fig. 3.1. The preliminary work in the DWT processor design is to build 1-D DWT modules, which are composed of high-pass and low-pass filters that perform a convolution of filter coefficients and input pixels. After a one-level of - discrete wavelet transform, the volume of image is decomposed into HHH, HHL, HLH, HLL, LHH, LHL, LLH and LLL signals as shown in the Fig. 3.1 [1].
Following figure show the gray level image. An image is used in many fields for example entertainment, remote sensing, medical image, etc. which is very useful for human life. In this thesis gray scale image is use for remove impulse noise.
Quantitative method is a method where data can be reduced to numbers or numerical values which is known as numerical analysis. The researcher needs to have a proper plan and data should be collected according to it. The main goal of this method is to develop and employ mathematical models, hypothesis related to our research. %the concerned phenomena%\cite{ashley}. Quantitative method has the following steps:
Image compression is the art and science of reducing the amount of data required to represent an image. The purpose for image compression is to reduce the amount of data required for representing sampled digital images and therefore reduce the cost for storage and transmission. Image compression plays a key role in many important applications, including image database, image communications, remote sensing.
Firstly, contrast of the image can be manipulated by adjusting the amplitude of the video signal. Amount of scatter radiation is the main factor to reduce the image intensified fluoroscopic contrast. Main causes of scatter radiation comes from scattered ionizing radiation, penumbral light scatter in the input and output screens and light scatter in the image intensification tube.