We will make image/picture compression using efficient fuzzy logic in this research. We will use quadtree algorithm for this purpose. We opt for fuzzy logic based method as fuzzy logic is considered strong tool to handle vagueness.
When images are vague in terms of pixel values fuzzy logic is considered appropriate logic for its analysis. In proposed technique one domain block is considered for every range block & searched only for matched contrast scaling. So outcomes fractal code does not contain coordinates of matched domain block. Quadtree algorithm may be here applied in such case & size of range block may be minimized as small as 2×2 pixels. proposed research deals with integration of quad tree algorithm with conventional DCT based fractal image compression in order to produce higher compression ratio PSNR with less compression error. Our main objective is to review the image of high compression and resolution and in order to compress it we use a novice algorithm and implements it in image so that the pixels of the image got compressed. In order to achieve this objective we use Fuzzy Logic.
In imaging science, image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. Image processing usually refers to digital image processing, but optical and analog image processing also are possible. The acquisition of images (producing the input image in the first place) is referred to as imaging. An image may be considered to contain sub-images sometimes referred to as regions-of-interest, ROIs, or simply regions.
This concept reflects the fact that images frequently contain collections of objects each of which can be the basis for a region. In a sophisticated image processing system it should be possible to apply specific image processing operations to selected regions. Thus one part of an image (region) might be processed to suppress motion blur while another part might be processed to improve color rendition. Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is among rapidly growing technologies today, with its applications in various aspects of a business.
Image Processing forms core research area within engineering and computer science disciplines too. Image processing basically includes the following three steps: 1. Importing the image with optical scanner or by digital photography.
2. Analyzing and manipulating the image which includes data compression and image enhancement and spotting patterns that are not to human eyes like satellite photographs. 3. Output is the last stage in which result can be altered image or report that is based on image analysis. Fundamental steps in digital image processing are Algorithm: In this implementation first of all the algorithm reads an image and defines the size of the range blocks & domain blocks. As per the defined size of range blocks and domain blocks the algorithm breaks the image in respective horizontal and vertical address of blocks. The image blocks of size 16 X 16 are saved as TP.
These blocks are further modified by reducing the pixel values by half. DCT is applied on each block and saved as TRR. Same operation is to be performed on domain blocks and they are saved as TD in a size of 32 X 32. Then the DCT of domain blocks are saved as TDM. Then the domain blocks are down sampled to the size of 16 X 16. Then the error between range blocks and domain blocks will be evaluated. As per the array values fuzzy logic will decide the no. of fractals prior to applying encoding the pixels.