The paper deals with studying techniques for reducing the storage required saving an image, or the bandwidth required transmitting it. Image compression addresses the problem of reducing the amount of data required to represent a digital image. The underlying basis of the reduction process is the removal of redundant data. From a mathematical viewpoint, this amounts 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. In this paper implementation of basic JPEG compression using only basic Matlab functions is pursued. This included going from a basic grayscale bitmap image all the way to a fully encoded file which is decoded to grayscale bitmap using decompression algorithm.
[...] y = blkproc(x, ' * x * P2't, P y = blkproc(y, ' round(x , y = im2col(y, ' distinct' % break 8*8 blocks into columns xb = size(y, % get number of blocks y = y(order, % reorder column elements eob = + % create end-of-block symbol r = zeros(numel(y) + size(y, count = for j = 1:xb % process 1 block (col) at a time i = max(find(y(:, % find last non-zero element if isempty(i) % no nonzero block values end p = count + q = p + = eob]; count =count + i + % truncate trailing add eob' end % and add to output vector r((count + % delete unused portion of r y.size = uint16([xm y.numblocks = uint16(xb); y.quality = uint16(quality * 100); y.col = y.eob = eob; B. THE FUNCTION TO COMPRESSED IMAGE. DECOMPRESS THE JPEG Fig.5 decompressed 8*8 subimage. Any differences between the original and reconstructed subimage are a result of lossy nature of JPEG compression and decompression process. XI. [...]
[...] 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, and inventory and product information over the internet. So the methods of compressing the data prior to storage and/or transmission are of significant practical and commercial interest. This work presents the compression of digital image using MATLAB by creating user defined functions. II. OVERVIEW The image compression systems are composed of two distinct structural blocks: an encoder and a decoder. [...]
[...] CONCLUSIONS The principle objective of this paper was to present the theoretic foundation of digital image compression and to implement the JPEG image compression. Two functions one for image compression and the other for image decompression were written in separate M files.These functions were used in another M file successfully to compress and decompress the image. The im2jpeg function converts the image into a single column. The simple column matrix obtained in im2jpeg was given to jpeg2im as an input and was decompressed to original image. [...]
[...] Function im2jpeg1(x, quality) % IM2JPEG compresses an image using a JPEG approximation y = im2jpeg(X,quality) compresses image X based on 8 * DCT tansforms, coefficient quantization, and huffman symbol % coding. input quality determines the amount of information % that is lost and compression achieved. Y is an encoding structure % containing fields: % y.size size of X % y.numblocks number of 8-by-8 encoded blocks % y.quality quality factor (as percent) if nargin [...]
[...] COMPRESSION PROCEDURE The image is first subdivided into pixel blocks of size which are processed left to right, top to bottom .As each 8*8 block or subimage is encountered, its 64 pixels level shifted by subtracting the quantity 2n-1, where 2n is maximum number of gray level. The 2-D discrete cosine transform of the block is a computed quantized in accordance with = round Where is a threshold and quantized approximation of and is an element of the transform normalization array of quantized coefficients And recorded, using the zigzag pattern to form a 1-D sequence .The brief summary can be, Consider compression and reconstruction of the following 8*8 subimage with the JPEG baseline standard VI. [...]
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