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Digital Image Processing

Dec 29, 2015




*Applied Lossy ans Lossless Image Compression on Matlab
*Spatial and Frequency Filter on Matlab

  • Project Report on

    Digital Image Processing


    Jodhpur 342001, INDIA

    By AkarshRastogi



    Digital image processing is the use of computer algorithms to

    perform image processing on digital images. As a subcategory

    or field of digital signal processing, digital image processing

    has many advantages over analog image processing. It allows

    a much wider range of algorithms to be applied to the input

    data and can avoid problems such as the build-up of noise and

    signal distortion during processing. Since images are defined

    over two dimensions digital image processing may be

    modeled in the form of multidimensional systems.


    Image Compression Lossy Image Compression

    JPEG image compession

    Lossless Image Compression

    Image Enhancement Spatial Intensity Transformation

    Frequency Filter

    Image Restoration Noise Estimation and their effects

    Weiner Filtering

  • Image Compression Image compression addresses the problem of reducing the amount of data

    required to represent a digital image. Compression is achieved by the removal

    of one or more of the three basic redundancies

    Coding redundancy, which is present when less than optimal code

    words are used

    Interpixel redundancy, which results from correlations between the

    pixels of an image

    Psychovisual redundancy, which is due to data that is ignored by the

    Human Visual System.

    Image compression can belossy or lossless.

    Lossy Compression are especially suitable for natural images such as

    photographs in applications where minor (sometimes imperceptible) loss of

    fidelity is acceptable to achieve a substantial reduction in bit rate.

    Lossless Compression is preferred for archival purposes and often for

    medical imaging, technical drawings, clip art or comics.

  • JPEG Image Compression JPEG is a commonly used method of lossy compression for digital images

    It includesfollowing steps:

    Construct nxnsubmatrices

    Image is a two dimensional M x N matrices . It is very difficult for computer

    to handle full image at once so the whole image is divided into


    Forward Transform

    Each sub image is transformed through Discrete Cosine Transform.


    This is the main step behind compression. Also, this is the step where

    major loss of data takes place. Quantization decreases the number of

    different values.

    Symbol Encoder

    JPEG encodes through Hoffman Encoding which further compresses the

    image. It finally outputs the compressed image.






    Quantizer Symbol







    nxn images






    image Decompressed


  • Symbol Decoder

    This is the first step while decoding the compressed image.

    Inverse Transform

    Then the inverse transform of the matrix is taken.

    Merge nxnsubimages

    Finally all the subimages are merged and the decompressed image is


    Matlab Code of JPEG %%%%%%%%%%%%%%%%%%image compression%%%%%%%%%%%%%%%%% %read original image original_image = imread ('IMG_5809.bmp'); %transform image to ycbcr format new_image= rgb2ycbcr(original_image); % seperating all 3 frames seperatly y_image=new_image(:,:,1); cb_image=new_image(:,:,2); cr_image=new_image(:,:,3); % changing image to small matrices cell_size= 8; repeat_height=floor(size(y_image,1)/cell_size); repeat_width=floor(size(y_image,2)/cell_size); repeat_height_mat = repmat(cell_size, [1 repeat_height]); repeat_width_mat = repmat(cell_size, [1 repeat_width]); y_subimg = mat2cell(y_image ,repeat_height_mat,repeat_width_mat); cb_subimg = mat2cell(cb_image ,repeat_height_mat,repeat_width_mat); cr_subimg = mat2cell(cr_image ,repeat_height_mat,repeat_width_mat); %discrete cosine transform fori=1:repeat_height for j=1:repeat_width y_subimg{i, j} = dct2(y_subimg{i, j});

  • cb_subimg{i, j} = dct2(cb_subimg{i, j}); cr_subimg{i, j} = dct2(cr_subimg{i, j}); end end %quantization quantization_factor = 16; fori=1:repeat_height for j=1:repeat_width y_subimg{i, j} = y_subimg{i, j} / quantization_factor; cb_subimg{i, j} = cb_subimg{i, j} / quantization_factor; cr_subimg{i, j} = cr_subimg{i, j} / quantization_factor; end end fori=1:repeat_height for j=1:repeat_width y_subimg{i, j}(:, 5:8) = 0; y_subimg{i, j}(5:8, :) = 0; cb_subimg{i, j}(:, 5:8) = 0; cb_subimg{i, j}(5:8, :) = 0; cr_subimg{i, j}(:, 5:8) = 0; cr_subimg{i, j}(5:8, :) = 0; end end y_compimg = uint8(cell2mat(y_subimg )) * quantization_factor; cb_compimg = uint8(cell2mat(cb_subimg)) * quantization_factor; cr_compimg = uint8(cell2mat(cr_subimg)) * quantization_factor; %to assign no of row comp_img=original_image; % now assign values to compressed image comp_img(:,:,1)=y_compimg; comp_img(:,:,2)=cb_compimg; comp_img(:,:,3)=cr_compimg; %%%%%%%%%%%%%%%%% image decompression %%%%%%%%%%%%%%%% %Dequanitzation fori=1:repeat_height for j=1:repeat_width y_subimg{i, j} = y_subimg{i, j} * quantization_factor; cb_subimg{i, j} = cb_subimg{i, j} * quantization_factor; cr_subimg{i, j} = cr_subimg{i, j} * quantization_factor; end end

  • %inverse dct fori=1:repeat_height for j=1:repeat_width y_subimg{i, j} = idct2(y_subimg{i, j}); cb_subimg{i, j} = idct2(cb_subimg{i, j}); cr_subimg{i, j} = idct2(cr_subimg{i, j}); end end %%Stich sub images into single image y_image = cell2mat(y_subimg); cb_image = cell2mat(cb_subimg); cr_image = cell2mat(cr_subimg); %%Convert to RGB space ycbcr_img(:, :, 1) = y_image; ycbcr_img(:, :, 2) = cb_image; ycbcr_img(:, :, 3) = cr_image; final_img = ycbcr2rgb(ycbcr_img); figure(1); imshow(original_img); figure(2); imshow(final_img);

  • Result of JPEG Compression

    Uncompressed Image (51.2 Mb)

    Compressed Image (36 Mb)

    akkuTypewritten textData lost after decompression=

    akkuTypewritten text15.2 Mb

  • Image Enhancement Quality of image can be enhanced through changes on the basis of histogram.

    Spatial Intensity Transformation

    Spatial domain techniques operate directly to the pixels of an image as

    opposed, for example to the frequency domain where operations are

    performed on the Fourier Transform of an image, rather than on image


    Basic Transformation Program on Matlab

    %% Basic Transformation on Matlab img=imread('IMG_5809.bmp'); %%Converting image to grayscale img=rgb2gray(img); %%Observing image and its histogram figure(1); imshow(img); figure(2); imhist(img); %%Histogram is towards right hand side hence colours are lighter new_img=img-50; %%new image and its histogram figure(3); imshow(new_img); figure(4); imhist(new_img); %%Equalising histogram newimage=histeq(img); figure(5); imshow(newimage); figure(6); imhist(newimage);

  • Result of spatial Transformation Initial image

    Initial Histogram

  • Final Image

    Final Histogram

  • After histogram equalization, image

    After histogram equalization, histogram

  • Frequency Filter

    Frequency filters process an image in the frequency domain. The image

    is Fourier transformed, multiplied with the filter function and then re-

    transformed into the spatial domain. Attenuating high frequencies results in a

    smoother image in the spatial domain, attenuating low frequencies enhances the


    All frequency filters can also be implemented in the spatial domain and, if there

    exists a simple kernel for the desired filter effect, it is computationally less

    expensive to perform the filtering in the spatial domain. Frequency filtering is

    more appropriate if no straightforward kernel can be found in the spatial

    domain, and may also be more efficient.

    Matlab Code for basic Frequency Filter

    I=imread('eight.tif'); J=imnoise(I,'gaussian',0,.01); [M,N]=size(J); u=0:M-1; v=0:N-1; idx= find(u>M/2); u(idx)=u(idx)-M; idy=find(v>N/2); v(idy)=v(idy)-N; [V,U]=meshgrid(v,u); K=fft2(J,M,N); D0=.05*2500; H=exp(-(U.^2 + V.^2)/(2*(D0^2))); G=(H).*(K); L=real(uint8(ifft2(G))); figure(1); imshow(J); figure(2); imshow(L);

  • Result of Low pass Frequency Filter

    Initial image

    Filtered image

  • Image Restoration The objective of image restoration is to improve image in some

    predefined sense. Restoration attempts to recover or reconstruct an

    image that has been degraded by using a priori knowledge of the

    degradation phenomenon. Thus restoration techniques are oriented

    toward modelling the degradation and applying the inverse process in

    order to recover the original image.

    Effect of noise on histogram

    I=imread('eight.tif'); A=imnoise(I,'gaussian'); B=imnoise(I,'poisson'); C=imnoise(I,'salt& pepper',.5); D=imnoise(I,'speckle'); figure(1); imhist(I); figure(2); imhist(A); figure(3); imhist(B); figure(4); imhist(C); figure(5); imhist(D);

    Original Histogram

  • Gaussian noise

    Poisson noise

  • Speckle noise

    Simple code of Weiner Filtering of a noised image

    I=imread('saturn.png'); I=rgb2gray(I); J=imnoise(I,'gaussian',0,0.05); K=wiener2(J,[10,10]); H=fspecial('disk',10); blurred=imfilter(J,H,'replicate'); figure(1); imshow(I); figure(2); imshow(J); figure(3); imshow(K); figure(4); imshow(blurred);

  • Resu