computer representation of images a picture function f(x,y) is a real-valued function of two...

Computer Representation of Images A picture function f(x,y) is a real-valued function of two variables, having values that are nonnegative and bounded, 0 f(x,y) L-1 for all (x,y) When a picture is digitized, a sampling process is used to extract from the picture a discrete set of samples, and then a quantization process is applied to these samples

Digital Image Representation CIS 465 Spring 2015

Sampling

Quantization

Computer Representation of Images The resultant digital picture function f, or digital picture, can be represented by a two-dimensional array of picture elements, or pixels The digital picture function f can be regarded as a mapping from {0,..., M-1} {0,..., N-1} to {0,..., L-1} The set {0,..., L-1} is called the gray level set, and L is the number of distinct gray levels For example, if we use 3 bits to represent each pixel, then L is 8 If 8 bits are used, L is 256.

Computer Representation of Images Picture Elements: Pixel Color, gray-value images and binary images (e.g., values 0 for black, 1 for white) Example gray-value images contain different number of brightness levels:

Computer Representation of Images M and N represent the size of the digital picture and are determined by the coarseness of the sampling If the sampling interval is too large, the process by which a digital image was produced may be apparent to human viewers This is the problem of undersampling A digital image may be generated by an image scanner, digital camera, CT scanner, etc.

Computer Representation of Images Even if two images have the same number of pixels, the quality of the images may differ in quality due to differences in how the images are captured More expensive digital cameras will have larger digital sensors than less expensive ones (larger sensors cost more) So if the two cameras produce images with the same number of pixels, the pixels in the larger array will represent a larger area so more information is packed into each pixel

Sensor Arrays of Differing Size

Image Resolution and Image Size Sometimes we use the term image resolution to refer to the size of the image in pixels this is imprecise (at best) The size of the image (and indirectly the image resolution) depends on the number of pixels per inch (along with size in pixels) associated with an image (scanner resolutions are typically quoted in ppi i.e. samples per inch) In a printer, where a number of dots may be needed to represent a pixel, we may use the term dots per inch To be still more precise, the image resolution (e.g. how well we can resolve separate lines in an image) depends on the device used to form the image

Image Resolution Test Pattern

1-bit Images Each pixel is stored as a single bit (0 or 1), so also referred to as a binary (or bilevel) image. Such an image is also called a 1-bit monochrome image since it contains no color. Fig. 3.1 shows a 1-bit monochrome image (called Lena by multimedia scientists - this is a standard image used to illustrate many algorithms).

1-bit image

Grey-scale image

Raster Display of Digital Images The dedicated RAM for storing the image frames is called the frame buffer The frame buffer is considered a component of the video cards or GPUs which are used to drive bit-mapped displays

Bit-mapped Displays Bit-mapped displays require a considerable amount of RAM. Historically, some common monitor sizes were 640480 (VGA), 800600 (SVGA), 1024768 (XVGA), and 1280960. Each of these has an aspect ratio of 4:3. 16:9 monitors are now the norm (e.g. 19201080) and resolutions continue to increase (e.g. 51202880 in ther Retina 27 iMac introduced in October 2014) To get true color, 8 bits are needed for each of the three primary colors, or 3 bytes/pixel. Thus, 1024x768 requires 2.3 MB of video RAM. An alternative to truecolor is hicolor which uses 16 bits per pixel (5 R, 6 G, 5 B or 5 for each with 1 bit unused)

Bit-mapped Displays To lessen this requirement, some computers have used 8-bits to indicate the desired color. This number is then used as an index into a hardware table called the color palette that contains 256 entries, each holding a 24-bit RGB value. This is called indexed color. It reduces the required RAM by 2/3, but allows only 256 colors. This technique is also called pseudocolor Sometimes each window on the screen has its own mapping. The palette is changed when a new window gains focus.

Bit-mapped Displays To display full-screen full-color multimedia on a 1024x768 display requires copying 2.3 MB of data to the video RAM for every frame. For full-motion video, 25 frame/sec is needed for a total data rate of 57.6 MB/sec. In liquid crystal displays (LCDs) as well as plasma display panels (PDPs), and digital micromirror displays (DMDs as in projectors) discrete pixels are constructed on the display device CRTs dont have this characteristic

Bit-mapped Displays Such a display can be driven digitally at the native pixel count (one to one correspondence between framebuffer pixels and display pixels) When there is a mismatch between framebuffer size and display size, the graphics system may resample by primitive means Drop or replicate pixels Image quality suffers in this case

Dithering When an image is printed, the basic strategy of dithering is used, which trades intensity resolution for spatial resolution to provide ability to print multi-level images on 2-level (1-bit) printers. Dithering is used to calculate patterns of dots such that values from 0 to 255 correspond to patterns that are more and more filled at darker pixel values, for printing on a 1-bit printer.

Dithering The main strategy is to replace a pixel value by a larger pattern, say 2 2 or 4 4, such that the number of printed dots approximates the varying- sized disks of ink used in analog, in halftone printing (e.g., for newspaper photos). 1. Half-tone printing is an analog process that uses smaller or larger filled circles of black ink to represent shading, for newspaper printing. 2. For example, if we use a 2 2 dither matrix

Dithering We can first re-map image values in 0..255 into the new range 0..4 by (integer) dividing by 256/5. Then, e.g., if the pixel value is 0 we print nothing, in a 2 2 area of printer output. But if the pixel value is 4 we print all four dots. The rule is: If the intensity is > the dither matrix entry then print an on dot at that entry location: replace each pixel by an n n matrix of dots. Note that the image size may be much larger, for a dithered image, since replacing each pixel by a 4 4 array of dots, makes an image 16 times as large.

Dithering A clever trick can get around this problem. Suppose we wish to use a larger, 4 4 dither matrix, such as

Dithering An ordered dither consists of turning on the printer output bit for a pixel if the intensity level is greater than the particular matrix element just at that pixel position. Fig. 3.4 (a) shows a grayscale image of Lena. The ordered-dither version is shown as Fig. 3.4 (b), with a detail of Lena's right eye in Fig. 3.4 (c).

Dithering

Picture Operations Digital images are transformed by means of one or more picture operations An operation transforms an image I into I An operation can be classified as a local operation if I(x,y) depends only on the values of some neighborhood of the pixel (x,y) in I If I(x,y) depends only on the value I(x,y), then the operation is called a point operation Operations for which the value of I(x,y) depends on all of the pixels of I are called global operations

Picture Operations An example of a local operation is an averaging operator which has the effect of blurring an image The new image I is found by replacing each pixel (x,y) in I by the average of (x,y) and (for example) the 8 neighbors of (x,y) Pixels on the edge of the image require special consideration

Picture Operations The gradient operation has the opposite effect - it sharpens a picture, emphasizing edges The gradient operator (and other local operators) can be illustrated by a template Imagine placing the center of the template on a given pixel (x,y) in I To compute (x,y) in I, multiply each neighbor by the corresponding value in the template and divide by the number of pixels in the template

Picture Operations Prewitt Operator: -1 0 1 1 1 1 -1 0 1 or 0 0 0 -1 0 1 -1 -1 -1 Sobel Operator: -1 0 11 2 1 -2 0 2 or 0 0 0 -1 0 1 -1 -2 -1

Sobel Operator

A Greyscale Image

Thresholding Thresholding is an example of a point operation Given a pixel value, we set the value of (x,y) in I to L-1 if (x,y) in I is greater than or equal to the threshold and to 0 if (x,y) is less than the threshold The resulting image has only two values and is thus a binary image For example, given a threshold value of 200 the image of the previous figure becomes the following

Binary Image

Global Operations Nonlocal operations are called global operations Examples include barrel distortion correction, perspective distortion correction and rotation Camera optics may cause barrel distortion. We can correct the distortion if we know a few control points

Barrel Distortion

The mappings for barrel distortion are: x = a 1 x + b 1 y + c 1 xy + d 1 y = a 2 x + b 2 y + c 2 xy + d 2 If we know four control points, we can solve for a 1, b 1 c 1, d 1, and a 2, b 2, c 2, d 2

Contrast Enhancement Contrast generally refers to a difference in grayscale values in some particular region of an image function Enhancing the contrast may increase the utility of the image. Suppose we have a digital image for which the contrast values do not fill the available contrast range. Suppose our data cover a range (m,M), but that the available range is (n,N). Then the following linear transformation expands the values over the available range g(x,y) = {[f(x,y)-m]/ (M-m)}[N-n]+n This transformation may be necessary when an image has been scanned, since the image scanner may not have been adjusted to use its full dynamic range

Contrast Enhancement For many classes of images, the ideal distribution of gray levels is a uniform distribution In general, a uniform distribution of gray levels makes equal use of each quantization level and tends to enhance low-contrast information We can enhance the contrast of an image by performing histogram equalization

Noise Removal Noise smoothing for snow removal in TV images was one of the first applications of digital image processing Certain types of noise are characteristic for pictures Noise arising from an electronic sensor generally appears as random, additive errors or snow In other situations, structured noise rather than random noise is persent in an image. Consider for example the scan line pattern of TV images which may be apparent to viewers

Image Data Types The most common data types for graphics and image file formats - 24-bit color and 8-bit color. Most image formats incorporate some variation of a compression technique due to the large storage size of image files. Compression techniques can be classified into either lossless or lossy. In a color 24-bit image, each pixel is represented by three bytes, usually representing RGB. Many 24-bit color images are actually stored as 32- bit images, with the extra byte of data for each pixel used to store an alpha value representing special effect information (e.g., transparency).

8-Bit Color Images - Pseudocolor As stated before, some systems only support 8 bits of color information in producing a screen image The idea used in 8-bit color images is to store only the index, or code value, for each pixel. Then, e.g., if a pixel stores the value 25, the meaning is to go to row 25 in a color look-up table (LUT).

8-Bit Color Images

How to Devise a Color Lookup Table The most straightforward way to make 8-bit look-up color out of 24-bit color would be to divide the RGB cube into equal slices in each dimension. The centers of each of the resulting cubes would serve as the entries in the color LUT, while simply scaling the RGB ranges 0..255 into the appropriate ranges would generate the 8-bit codes. Since humans are more sensitive to R and G than to B, we could use 3 bits for R and G, and 2 bits for B Can lead to edge artifacts though

How to Devise a CLUT Median-cut algorithm: A simple alternate solution that does a better job for this color reduction problem. (a) The idea is to sort the R byte values and find their median; then values smaller than the median are labelled with a 0 bit and values larger than the median are labelled with a 1 bit. (b) This type of scheme will indeed concentrate bits where they most need to differentiate between high populations of close colors. (c) One can most easily visualize finding the median by using a histogram showing counts at position 0..255. (d) Fig. 3.11 shows a histogram of the R byte values for the forestfire.bmp image along with the median of these values, shown as a vertical line.

Median-Cut Algorithm

Popular Image File Formats We will look at GIF JPEG TIFF EXIF PS PDF

GIF GIF standard: (We examine GIF standard because it is so simple yet contains many common elements.) Limited to 8-bit (256) color images only, which, while producing acceptable color images, is best suited for images with few distinctive colors (e.g., graphics or drawing). GIF standard supports interlacing - successive display of pixels in widely-spaced rows by a 4-pass display process. GIF actually comes in two flavors: 1. GIF87a: The original specification. 2. GIF89a: The later version. Supports simple animation via a Graphics Control Extension block in the data, provides simple control over delay time, a transparency index, etc.

GIF Originally developed by UNISYS corporation and Compuserve for platform-independent image exchange via modem Compression using the Lempel-Ziv-Welch algorithm (LZW) slightly modified Localizes bit patterns which occur repeatedly Variable bit length coding for repeated bit patterns Well suited for image sequences (can have multiple images in a file)

GIF87 File format overview:

GIF87 Screen Descriptor comprises a set of attributes that belong to every image in the file. According to the GIF87 standard, it is defined as in Fig. 3.13.

GIF87 Color Map is set up in a very simple fashion as in Fig. 3.14. However, the actual length of the table equals 2 (pixel+1) as given in the Screen Descriptor.

GIF87 Each image in the file has its own image descriptor

GIF 87 Interlaced Display Mode

JPEG JPEG: The most important current standard for image compression. The human vision system has some specific limitations and JPEG takes advantage of these to achieve high rates of compression. JPEG allows the user to set a desired level of quality, or compression ratio (input divided by output).

PNG PNG format: standing for Portable Network Graphics: meant to supersede the GIF standard, and extends it in important ways. Special features of PNG files include: 1. Support for up to 48 bits of color information - a large increase. 2. Files may contain gamma-correction information for correct display of color images, as well as alpha- channel information for such uses as control of transparency. 3. The display progressively displays pixels in a 2- dimensional fashion by showing a few pixels at a time over seven passes through each 8 8 block of an image.

TIFF TIFF: stands for Tagged Image File Format. The support for attachment of additional information (referred to as tags) provides a great deal of flexibility. 1. The most important tag is a format signifier: what type of compression etc. is in use in the stored image. 2. TIFF can store many different types of image: 1-bit, grayscale, 8-bit color, 24-bit RGB, etc. 3. TIFF was originally a lossless format but now a new JPEG tag allows one to opt for JPEG compression. 4. The TIFF format was developed by the Aldus Corporation in the 1980's and was later supported by Microsoft.

EXIF EXIF (Exchange Image File) is an image format for digital cameras: 1. Compressed EXIF files use the baseline JPEG format. 2. A variety of tags (many more than in TIFF) are available to facilitate higher quality printing, since information about the camera and picture-taking conditions (flash, exposure, light source, white balance, type of scene, etc.) can be stored and used by printers for possible color correction algorithms. 3. The EXIF standard also includes specification of file format for audio that accompanies digital images. As well, it also supports tags for information needed for conversion to FlashPix (initially developed by Kodak).

Postscript History: Developed 1984 by Adobe First time fonts became important to the general public Functionality: Integration of high-quality text, graphics and images programming language full-fledged with variables, control structures and files Vector based, can include bit-mapped graphics Encapsulated PS for inclusion in other files

Postscript Postscript Level-1: Earliest version developed in 1980s Scalable font concept (in contrast to fixed-size fonts available until then) Problem: no patterns available to fill edges of letters resulting in medium quality Postscript Level-2: High-quality pattern filling Greater number of graphics primitives Color concept both device-dependent or device-independent ASCII files Follow-up: Adobes Portable Document Format (PDF) LZW compression

Windows Metafile Microsoft Windows: WMF: the native vector file format for the Microsoft Windows operating environment: 1. Consist of a collection of GDI (Graphics Device Interface) function calls, also native to the Windows environment. 2. When a WMF file is played (typically using the Windows PlayMetaFile() function) the described graphics is rendered. 3. WMF files are ostensibly device-independent and are unlimited in size.

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