JPEG compression algorithm implementation in MATLAB
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jpegCompress.m function y = jpegCompress(x, quality) % y = jpegCompress(x, quality) compresses an image X based on 8 x 8 DCT % transforms, coefficient quantization and Huffman symbol coding. Input % quality determines the amount of information that is lost and compression achieved. y is the encoding structure containing fields: % y.size size of x % y.numblocks number of 8 x 8 encoded blocks % y.quality quality factor as percent % y.huffman Huffman coding structure
narginchk(1, 2); % check number of input arguments if ~ismatrix(x) ~isreal(x) ~ isnumeric(x) ~ isa(x, 'uint8') error('The input must be a uint8 image.'); end if nargin < 2 quality = 1; % default value for quality end if quality <= 0 error('Input parameter QUALITY must be greater than zero.'); end
m = [16 11 10 16 24 40 51 61 % default JPEG normalizing array 12 12 14 19 26 58 60 55 % and zig-zag reordering pattern 14 13 16 24 40 57 69 56 14 17 22 29 51 87 80 62 18 22 37 56 68 109 103 77 24 35 55 64 81 104 113 92 49 64 78 87 103 121 120 101 72 92 95 98 112 100 103 99] * quality;
order = [1 9 2 3 10 17 25 18 11 4 5 12 19 26 33 ... 41 34 27 20 13 6 7 14 21 28 35 42 49 57 50 ... 43 36 29 22 15 8 16 23 30 37 44 51 58 59 52 ... 45 38 31 24 32 39 46 53 60 61 54 47 40 48 55 ... 62 63 56 64];
[xm, xn] = size(x); % retrieve size of input image x = double(x) - 128; % level shift input t = dctmtx(8); % compute 8 x 8 DCT matrix
% Compute DCTs pf 8 x 8 blocks and quantize coefficients y = blkproc(x, [8 8], 'P1 * x * P2', t, t'); y = blkproc(y, [8 8], 'round(x ./ P1)', m); % <== nearly all elements from y are zero after this step y = im2col(y, [8 8], 'distinct'); % break 8 x 8 blocks into columns xb = size(y, 2); % get number of blocks y = y(order, :); % reorder column elements
eob = max(x(:)) + 1; % create end-of-block symbol r = zeros(numel(y) + size(y, 2), 1); count = 0;
for j = 1:xb % process one block(one column) at a time i = find(y(:, j), 1, 'last'); % find last non-zero element if isempty(i) % check if there are no non-zero values i = 0; end p = count + 1; q = p + i; r(p:q) = [y(1:i, j); eob]; % truncate trailing zeros, add eob count = count + i + 1; % and add to output vector end
r((count + 1):end) = []; % delete unused portion of r
y = struct; y.size = uint16([xm xn]); y.numblocks = uint16(xb); y.quality = uint16(quality * 100); y.huffman = mat2huff(r);
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