ENEE408G Multimedia Signal Processing

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1 ENEE48G Multimedia Signal Processing Design Project on Image Processing and Digital Photography Goals:. Understand the fundamentals of digital image processing.. Learn how to enhance image quality and how to compress images. 3. Explore artistic techniques using digital image processing. Note: The symbol means to put your discussion, flowchart, block diagram, or plots in your report. The symbol indicates that you should put the multimedia data in your report. Save images in BMP format unless otherwise stated. The symbol means to put your source codes (Matlab or C/C++) in your report. I. Color Coordinate Systems Several color coordinate systems are commonly used in practice. Each coordinate system represents a color space consisting of several components and has its own special purpose. In this section, we explore three color coordinate systems: RGB (Red, Green and Blue), HSL (Hue, Saturation and Lightness), and YUV. Using these coordinate systems, we can separate the components of a color image.. Open the file Flower.bmp using Paint Shop Pro. To split a color image into red, green and blue components, select Image Split channel Split to RGB. What do you observe from these three images? Choose the red component and adjust its value by selecting Adjust Brightness/Contrast. Set the Brightness to 75 and Contrast to. Then, combine this new red component and the original green and blue components by selecting Image Combine Channel Combine from RGB. Observe and store this combined image. Again, split Flower.bmp into RGB components, but instead brighten the green component. Again, split Flower.bmp into RGB components, but instead brighten the blue component. Compare these three new images and the original image. Describe what you observe. UMD ENEE48G Ver. 68

2 . Next we examine a different color coordinate system. Open Flower.bmp, and change the color coordinate to HSL by selecting Image Split channel Split to HSL and Image Combine channel Combine from HSL. Repeat the procedure from Part, i.e. brighten the Hue, Saturation, and Lightness components, separately. Save these new images. Discuss the role that each component plays in this color coordinate system. 3. Now we examine the YCbCr (YUV) color coordinate using Matlab. Write a Matlab script to do the following: RGB to YCbCr: Use imread.m and rgbycbcr.m to separate Flower.bmp into Y, Cb, and Cr. Downsampling: Use imresize.m to downsample Cb and Cr components in each dimension by factors, 4, and 8. Upsampling: Use imresize.m to upsample the downsampled Cb and Cr components to their original sizes. YCbCr to RGB: Combine Y and these two new components, Cb and Cr, and transform back to the RGB color coordinate using ycbcrrgb.m. Display these new images using imshow.m. Save these images using imwrite.m. Explain what you observe and discuss what advantages the color coordinate transform from RGB to YUV has. Compare RGB and YUV representations. Under what situation will you adopt each color representation? 4. In a TV weather report, the viewer sees a weatherman standing in front of a map or some other computer-generated image. In reality, the weatherman is standing in front of a blue (or green) curtain. A special camera system will extract the weatherman s image and superimpose it upon a computer-generated weather related image. The basic idea of this camera system is to split an image into RGB channels, create a mask based on the information of blue channel, and use this mask to extract the desired image. Use Paint Shop Pro to extract the flower object from BlueBG.bmp, and superimpose this flower object upon a background image, Sand.jpg. Draw a block diagram of your scheme and describe the procedures you followed with Paint Shop Pro. Save the final image of the flower with sand. UMD ENEE48G Ver. 68

3 II. Digital Halftoning Halftoning converts grayscale or color images to two-level (i.e. binary) images. This technique is widely used in printing. In this section, we learn how to generate a halftone image using Paint Shop Pro and Matlab.. Paint Shop Pro supports several halftone methods. Select Image Decrease Color Depth Colors. You will see the following dialog box. Set Palette component as Grey values and Palette weight as non-weighted. Apply the five reduction methods shown in the dialog box on the images Lena.bmp and Baboon.bmp. Save these images and compare them.. We can generate a halftone image in other ways. Most significant bit approach: We can simply select the most significant bit to represent black and white. Select Adjust Brightness and Contrast Threshold, and set the threshold to 7. Save the image. Dithering approach: Before applying a threshold, we can add noise by selecting Adjust Add/Remove Noise Add Noise. Choose 5% uniform noise. After adding noise, apply a threshold of 7 as mentioned in the first approach. Save this image. What happens if you apply a different magnitude percentage and different types of random noise? Halftone screen approach: Open the two images, Lena.bmp and halftone_screen.tif, and select Image Arithmetic. Add the two images using the settings shown below. After finishing this step, use a threshold of 55 to generate a two-level halftone image. Compare the results of all three halftoning approaches. UMD ENEE48G Ver. 68 3

4 3. Now you design your own halftone screen. First, design an 8x8 halftone pattern matrix M. Then use the function, halftone_screen.m, to generate your own halftone screen. There are four parameters in this function. % halftone_screen( M, im_height, im_width, filename) % M: a matrix you have designed; for M= (scalar), it sets a default clustered-dot % pattern; for M=, it sets a dispersed dot pattern. % im_heigth and im_width: the dimension of the source image. % filename: the name and storage path of halftone screen. Use this halftone screen to generate halftoned Lena and Baboon. Save the images. Compare the results with the halftoning approaches in the previous section. 4. Color Halftoning: For this task, we will use the CMYK color coordinate system. RGB is an additive color synthesis system commonly used for color displays. CMY is a subtractive color synthesis system for pigment colors (e.g. newspapers, color printing). CMY is the complementary system of RGB, and they are related by C=-R, M=-G, Y=-B. The CMYK system is the offspring of CMY. The difference between CMYK and CMY is that CMYK extracts all black elements from CMY and put in the last component, black. Separate LenaC.bmp into the CMYK (Cyan-Magenta-Yellow-blacK) color coordinate system using Image Split channel Split to CMYK. Here, we obtain 4 color components. Perform the halftoning technique on each component and set these components as gray level images using Image Greyscale. Combine these four new images by Image Combine Channel Combine from CMYK. Thus, we obtain the color halftone image. Save the image. UMD ENEE48G Ver. 68 4

5 III. Image Enhancement An image could be corrupted during transmission or captured in undesirable conditions. We can use image enhancement techniques to enhance some features and improve the visual quality of an image.. The histogram of a grayscale image shows its luminance distribution. We examine the histogram of the grayscale image Lena using Paint Shop Pro. Open Lena.bmp and select View Palettes Histogram (or press F7), and you will see a histogram window. Histogram equalization is an image enhancement technique to make the luminance distribution more uniform. Select Adjust Brightness and Contrast Histogram Equalize to perform histogram equalization. Apply this technique to both Lena.bmp and LenaDark.bmp. Observe the histogram before and after equalization. Save the equalized images. Record the mean and median values of the original and equalized versions of both images. Histogram stretching is a technique that stretches the luminance distribution using the range of possible luminance values while maintaining the overall shape of the original distribution. Select Adjust Brightness and Contrast Histogram Stretch to perform histogram stretching on both Lena.bmp and LenaDark.bmp. Save the stretched images. Compare the results of histogram stretching with that of histogram equalization.. In some situations, we would like to emphasize a specific range of luminance values. In Paint Shop Pro, we can shape the histogram of an image to a desired histogram using Histogram Adjustment using a few methods. One method involves shaping the midtones of a histogram by performing a non-linear mapping between the luminance values of the input image and that of the output image. When we compress the midtones, we map luminance values toward the middle band (i.e. toward an intermediate gray level). When we expand the midtones, we map luminance values toward the extremes (i.e. toward black and white). The gamma value also controls a non-linear mapping between input and output luminance values. The response of photographic films is non-linear and can be written as d = γ log w d. w is the incident light intensity. d is the optical density, which is the response on the film UMD ENEE48G Ver. 68 5

6 for w. γ is called the gamma of the film. A similar nonlinear response is also associated with the visual display. We can compensate for the non-linear response using an inverse procedure which appropriately adjusts the gamma, γ. Open Lena.bmp. Select Adjust Brightness and Contrast Histogram Adjustment, and you will see the Histogram Adjustment dialog box. Adjust the values in Midtones and Gamma and observe the resulting image. Now open LenaDark.bmp. Adjust the values in Midtones and Gamma and observe the resulting image. Save this reshaped image. Record the values of parameters you used. Compare your result with the results of histogram equalization and stretch. 3. Image sharpening is a class of enhancement techniques applied to a moderately blurred image to make soft edges and transitions sharper. Open LenaBlur.bmp. Select Adjust Sharpness Sharpen and Sharpen More to improve the quality of this image. Observe the effect and save the sharpened image. Discuss whether a blurred image can be completely recovered. One approach to implement image sharpening is to use a spatial high-boost filter. Select Effects User Defined Filter. Enter the parameters shown below and apply the filter to LenaBlur.bmp. Save this sharpened image. UMD ENEE48G Ver. 68 6

7 In general, we can create an m x m spatial high-boost filter as follows: w m Where w= A m and A Generate 5x5 and 7x7 high-boost filters to improve LenaBlur.bmp. Save these improved images. Record the m and A you have used in each case. Compare the output image quality with that of the previous 3x3 high-boost filter. 4. An image can become noisy in such situations as transmission across a noisy channel. In this task, we examine techniques that remove noise and recover the original perceptual quality of the image. There are two popular noise models widely used in image processing. One is Gaussian noise and the other is salt-and-pepper noise. Here, we use two approaches, a spatial averaging filter (i.e. low pass filter) and a median filter, to remove these two kinds of noises. (Note: BoatPxx.tif represents an image that suffers from salt-and-pepper noise and BoatGxx.tif suffers from Gaussian noise.) Clean salt-and-pepper noise using an averaging filter: Open BoatP5.tif, BoatP5.tif, and BoatP5.tif. Select Adjust Blur Average. Set Filter aperture to 3. Save the filtered images. Clean salt-and-pepper noise using a median filter: Open BoatP5.tif, BoatP5.tif, and BoatP5.tif. Select Adjust Add/Remove Noise Median Filter. Adjust the Filter Aperture to enhance the photo. Save the filtered images. Clean Gaussian noise using an averaging filter: Open BoatG.tif, BoatG.tif, and BoatG.tif, and use an averaging filter. Save the filtered images. Clean Gaussian noise using a median filter: Open BoatG.tif, BoatG.tif, and BoatG.tif, and use a median filter. Save the filtered images. Observe these results. For each type of noise, explain which filter best removes the noise. 5. Edges are local discontinuities in luminance. These important features play an important role in many image processing and computer vision tasks. Here, we experiment with a few edge detection algorithms. UMD ENEE48G Ver. 68 7

8 Apply Effects Edge Effects Find All to Pepper.bmp and Baboon.bmp. Save these images. There are several spatial edge detectors that we can study through the User Defined Filter mentioned earlier. Apply the following six filters to Pepper.bmp and Baboon.bmp and select Image Arithmetic Add to add the corresponding row-directional and column-directional (or antidiagonal-directional and diagonal-directional) filter outputs. Save these images. Filter type Antidiagonal-Directional Filter Diagonal-Directional Filter Roberts Filter type Row-Directional Filter Column-Directional Filter Prewitt 3 3 Sobel 4 4 Compare these results with those obtained using Find All. Which filter has the best performance? IV: Digital Photography During the past few years, the power of digital cameras has improved. For example, we can view or delete images immediately after capture, modify imperfect images, and transfer images easily through electronic connections. Use the digital camera to take pictures, and modify them artistically and creatively. Describe any processing involved. Feel free to use the built-in functions of the digital camera to do real-time processing. Apply the skills you have learned from Paint Shop Pro and Matlab to do post-processing. UMD ENEE48G Ver. 68 8

9 V. Image Compression JPEG JPEG (Joint Photography Experts Group) is a popular image compression format. The JPEG encoder can compress images into a small file size without introducing too much noticeable distortion. JPEG supports several modes to display images. For example, the progressive mode can display an image progressively from coarse to fine. In other words, if the connection speed of a network is slow, users can view a low-quality version of the image first and then fine details will be included later. We first explore JPEG compression, and then we design a JPEG-like image codec.. In this experiment, we compare different parameters in the JPEG standard using Paint Shop Pro. To save an image in JPEG format, select File Save As. Under Save as type, choose JPEG. Click the Options button. A dialog box will appear. In many implementations of JPEG, users can select a quality factor (Q) to generate an image with higher quality at the expense of a larger file size. The quality factor is inversely correlated to the quantization scale factor, which is a multiplicative factor applied to the JPEG quantization table. Paint Shop Pro uses a value called the compression factor (CF) instead of the quality factor. The compression factor is directly correlated to the quantization scale factor. Standard Mode: Save LenaC.bmp as standard mode. Choose a compression factor of,, 3,, 9 and record the resulting image sizes. Discuss the relationship between the compression factor and the image quality. Zoom into the image. Describe the artifacts caused by the 8x8 blocks. Try Adjust Add/Remove Noise JPEG Artifact Removal to remove the artifacts. Progressive Mode: Open Mozilla Firefox. (There is a documented bug in Internet Explorer that prevents proper display of progressive images.) Open the images wash-ir_seq.jpg (encoded using JPEG sequential mode) and wash-ir_prog.jpg (encoded using JPEG progressive mode) in Firefox; both images are accessible from the course website. Observe how these images are loaded and displayed.. Here, we design a JPEG-like image codec and implement it by writing two Matlab functions, one for the encoder and one for the decoder. The block diagram is shown below. represents downsampling by. represents upsampling by. A useful tutorial of the JPEG standard can be downloaded at ftp://ftp.uu.net/graphics/jpeg/wallace.ps.gz. UMD ENEE48G Ver. 68 9

10 Y DCT Q Y Zigzag RGB Image RGB to YCbCr Cb DCT Q Cb Zigzag Entropy Encoding Compressed Image Cr DCT Q Cr Zigzag Encoder izigzag iq Y IDCT Y Compressed Image Entropy Decoding izigzag iq Cb IDCT Cb YCbCr to RGB Reconstructed RGB Image izigzag iq Cr IDCT Cr Decoder Here are some notes on implementing key modules. Image I/O: Use imread.m and imwrite.m. RGB to YCbCr: Use rgbycbcr.m and ycbcrrgb.m. To downsample or upsample, you can use imresize.m or simple spatial sampling and average of image. The DCT block refers to an NxN block-based DCT. You may use dct.m and blkproc.m. IDCT refers to the NxN block-based inverse DCT. Use idct.m and blkproc.m. Q Y, Q Cb, and Q Cr represent the NxN quantization with quantization tables for luminance and chrominance components, respectively, and iq Y, iq Cb, iq Cr denote the corresponding inverse quantization tables. JpegLumQuanTable.m returns the JPEG standard luminance quantization table, and JpegChrQuanTable.m returns the JPEG standard chrominance quantization table. In this part, you should use the JPEG standard table as a reference and design your own table. You can use ZigzagMtxVector.m that is provided to you to perform zigzag scanning and use VectorZigzagMtx.m for izigzag. UMD ENEE48G Ver. 68

11 For Entropy Encoding, use the JPEG_entropy_encode.m module provided. This function will read a matrix, in which each row represents a vectorized DCT block, write a bit stream whose filename is always named as JPEG.jpg, and return the length of this file. JPEG_entropy_encode.m is an interface for generating a text file, JPEG_DCTQ_ZZ.txt, and running jpeg_entropy_encode.exe. You can do the luminance part first, rename this file, and then do the chrominance part. For the entropy decoding, use JPEG_entropy_decode.m, which performs the inverse functionality. Make sure that the pixel value of the reconstructed image are integers within [ 55]. If the value is above 55, you should threshold it to 55. If the value is below, threshold it to. Write two functions in Matlab to implement the JPEG encoder and decoder. 3. Adjust the following parameters (or matrix) on LenaC.bmp: N, Q Y, Q Cb, and Q Cr. Use the following definitions to plot the PSNR of luminance component (y-axis) vs. CR (x-axis). Make sure that your plot contains a sufficient number of data points. Peak signal to noise ratio (PSNR): where peak_intensity = 55, w is the width of the image, h is the height of the image, and err[i][j] represents the difference between the (i, j) pixel in the original image and that of the reconstructed image. Compression ratio (CR): ( peak _ intensity) PSNR = MSD MSD = w i h j ( err[ i][ j]) w h CR = original image file size (in bytes) / compressed image file size (in bytes) Remember that a compressed image should contain both luminance and chrominance data, though we store them in several files here for simplicity. Your codec should achieve as high PSNR as possible for each compression ratio. 4. Here, we examine the impact of zigzag scanning. Remove the Zigzag and izigzag modules from your codec. Once again, plot the PSNR vs. CR, and compare this plot with your previous PSNR vs. CR curve. Discuss the advantage of zigzag scanning. UMD ENEE48G Ver. 68

12 VI: Mobile Computing and Pocket PC Programming From the above exercises, we have learned some fundamentals of image processing. Now design a simple Pocket PC application related to digital image processing using the Microsoft embedded Tools. You can refer to ENEE48G Multimedia Signal Processing Mobile Computing and Pocket PC Programming Manual and extend the examples there. Please include sufficient documentation along with your source code and an executable. UMD ENEE48G Ver. 68

13 For Further Exploration Many techniques of digital image processing are used extensively in various ways of art and entertainment. In this part, we will learn some special effects used in those fields.. Emboss Filter: A spatial filter is not limited to sharpening and blurring. Many digital artists use a spatial filter, such as the emboss filter, to create artwork. Apply an emboss filter on Lena.bmp by selecting Effects Texture Effects Emboss in Paint Shop Pro. Save the image. We can implement the emboss filter simply by selecting Effects User Defined Filter. Set the Bias of the filter as 8 and Filter Matrix as Save the image. We can apply the same emboss filter on a color image, such as LenaC.bmp. Split the image into HSL color coordinates. Use the previous emboss filter to filter the L component. Combine these three components. Save the image.. Lithograph and Psychedelic Distillation Filter: Try the following two filters on LenaC.bmp using User Defined Filter. Save the results. Lithograph Psychedelic Distillation Division Factor: ; Bias : Division Factor: ; Bias : We have learned several useful spatial filters to create artistic effects. The basic approach is to sum the weighted neighbors around each pixel. In this part, design one special filter to create your own artistic effect. Provide the filter coefficients. Save the image(s). UMD ENEE48G Ver. 68 3

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