Digital Image Processing Chapter 6: Color Image Processing
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What Resolution Should Your Images Be?
What Resolution Should Your Images Be? The best way to determine the optimum resolution is to think about the final use of your images. For publication you’ll need the highest resolution, for desktop printing lower, and for web or classroom use, lower still. The following table is a general guide; detailed explanations follow. Use Pixel Size Resolution Preferred Approx. File File Format Size Projected in class About 1024 pixels wide 102 DPI JPEG 300–600 K for a horizontal image; or 768 pixels high for a vertical one Web site About 400–600 pixels 72 DPI JPEG 20–200 K wide for a large image; 100–200 for a thumbnail image Printed in a book Multiply intended print 300 DPI EPS or TIFF 6–10 MB or art magazine size by resolution; e.g. an image to be printed as 6” W x 4” H would be 1800 x 1200 pixels. Printed on a Multiply intended print 200 DPI EPS or TIFF 2-3 MB laserwriter size by resolution; e.g. an image to be printed as 6” W x 4” H would be 1200 x 800 pixels. Digital Camera Photos Digital cameras have a range of preset resolutions which vary from camera to camera. Designation Resolution Max. Image size at Printable size on 300 DPI a color printer 4 Megapixels 2272 x 1704 pixels 7.5” x 5.7” 12” x 9” 3 Megapixels 2048 x 1536 pixels 6.8” x 5” 11” x 8.5” 2 Megapixels 1600 x 1200 pixels 5.3” x 4” 6” x 4” 1 Megapixel 1024 x 768 pixels 3.5” x 2.5” 5” x 3 If you can, you generally want to shoot larger than you need, then sharpen the image and reduce its size in Photoshop. -
Invention of Digital Photograph
Invention of Digital photograph Digital photography uses cameras containing arrays of electronic photodetectors to capture images focused by a lens, as opposed to an exposure on photographic film. The captured images are digitized and stored as a computer file ready for further digital processing, viewing, electronic publishing, or digital printing. Until the advent of such technology, photographs were made by exposing light sensitive photographic film and paper, which was processed in liquid chemical solutions to develop and stabilize the image. Digital photographs are typically created solely by computer-based photoelectric and mechanical techniques, without wet bath chemical processing. The first consumer digital cameras were marketed in the late 1990s.[1] Professionals gravitated to digital slowly, and were won over when their professional work required using digital files to fulfill the demands of employers and/or clients, for faster turn- around than conventional methods would allow.[2] Starting around 2000, digital cameras were incorporated in cell phones and in the following years, cell phone cameras became widespread, particularly due to their connectivity to social media websites and email. Since 2010, the digital point-and-shoot and DSLR formats have also seen competition from the mirrorless digital camera format, which typically provides better image quality than the point-and-shoot or cell phone formats but comes in a smaller size and shape than the typical DSLR. Many mirrorless cameras accept interchangeable lenses and have advanced features through an electronic viewfinder, which replaces the through-the-lens finder image of the SLR format. While digital photography has only relatively recently become mainstream, the late 20th century saw many small developments leading to its creation. -
Fi-4750C Image Scanner Operator's Guide Fi-4750C Image Scanner Operator's Guide Revisions, Disclaimers
C150-E182-01EN fi-4750C Image Scanner Operator's Guide fi-4750C Image Scanner Operator's Guide Revisions, Disclaimers Edition Date published Revised contents 01 September, 2000 First edition Specification No. C150-E182-01EN FCC declaration: This equipment has been tested and found to comply with the limits for a Class B digital device, pursuant to Part 15 of the FCC Rules. These limits are designed to provide reasonable protection against harmful interference in a residential installation. This equipment generates, uses, and can radiate radio frequency energy and, if not installed and used in accordance with the instruction manual, may cause harmful interference to radio communications. However, there is no guarantee that interference will not occur in a particular installation. If this equipment does cause harmful interference to radio or television reception, which can be determined by turning the equipment off and on, the user is encouraged to try to correct the interference by one or more of the following measures: • Reorient or relocate the receiving antenna. • Increase the separation between the equipment and receiver. • Connect the equipment into an outlet on a circuit different from that to which the receiver is connected. • Consult the dealer or an experienced radio/TV technician for help. FCC warning: Changes or modifications not expressly approved by the party responsible for compliance could void the user's authority to operate the equipment. NOTICE • The use of a non-shielded interface cable with the referenced device is prohibited. The length of the parallel interface cable must be 3 meters (10 feet) or less. The length of the serial interface cable must be 15 meters (50 feet) or less. -
Binary Image Compression Algorithms for FPGA Implementation Fahad Lateef, Najeem Lawal, Muhammad Imran
International Journal of Scientific & Engineering Research, Volume 7, Issue 3, March-2016 331 ISSN 2229-5518 Binary Image Compression Algorithms for FPGA Implementation Fahad Lateef, Najeem Lawal, Muhammad Imran Abstract — In this paper, we presents results concerning binary image compression algorithms for FPGA implementation. Digital images are found in many places and in many application areas and they display a great deal of variety including remote sensing, computer sciences, telecommunication systems, machine vision systems, medical sciences, astronomy, internet browsing and so on. The major obstacle for many of these applications is the extensive amount of data required representing images and also the transmission cost for these digital images. Several image compression algorithms have been developed that perform compression in different ways depending on the purpose of the application. Some compression algorithms are lossless (having same information as original image), and some are lossy (loss information when compressed). Some of the image compression algorithms are designed for particular kinds of images and will thus not be as satisfactory for other kinds of images. In this project, a study and investigation has been conducted regarding how different image compression algorithms work on different set of images (especially binary images). Comparisons were made and they were rated on the bases of compression time, compression ratio and compression rate. The FPGA workflow of the best chosen compression standard has also been presented. These results should assist in choosing the proper compression technique using binary images. Index Terms—Binary Image Compression, CCITT, FPGA, JBIG2, Lempel-Ziv-Welch, PackBits, TIFF, Zip-Deflate. —————————— —————————— 1 INTRODUCTION OMPUTER usage for a variety of tasks has increased im- the most used image compression algorithms on a set of bina- C mensely during the last two decades. -
Chapter 3 Image Data Representations
Chapter 3 Image Data Representations 1 IT342 Fundamentals of Multimedia, Chapter 3 Outline • Image data types: • Binary image (1-bit image) • Gray-level image (8-bit image) • Color image (8-bit and 24-bit images) • Image file formats: • GIF, JPEG, PNG, TIFF, EXIF 2 IT342 Fundamentals of Multimedia, Chapter 3 Graphics and Image Data Types • The number of file formats used in multimedia continues to proliferate. For example, Table 3.1 shows a list of some file formats used in the popular product Macromedia Director. Table 3.1: Macromedia Director File Formats File Import File Export Native Image Palette Sound Video Anim. Image Video .BMP, .DIB, .PAL .AIFF .AVI .DIR .BMP .AVI .DIR .GIF, .JPG, .ACT .AU .MOV .FLA .MOV .DXR .PICT, .PNG, .MP3 .FLC .EXE .PNT, .PSD, .WAV .FLI .TGA, .TIFF, .GIF .WMF .PPT 3 IT342 Fundamentals of Multimedia, Chapter 3 1-bit Image - binary image • Each pixel is stored as a single bit (0 or 1), so also referred to as binary image. • Such an image is also called a 1-bit monochrome image since it contains no color. It is satisfactory for pictures containing only simple graphics and text. • A 640X480 monochrome image requires 38.4 kilobytes of storage (= 640x480 / (8x1000)). 4 IT342 Fundamentals of Multimedia, Chapter 3 8-bit Gray-level Images • Each pixel has a gray-value between 0 and 255. Each pixel is represented by a single byte; e.g., a dark pixel might have a value of 10, and a bright one might be 230. • Bitmap: The two-dimensional array of pixel values that represents the graphics/image data. -
1/2-Inch Megapixel CMOS Digital Image Sensor
MT9M001: 1/2-Inch Megapixel Digital Image Sensor Features 1/2-Inch Megapixel CMOS Digital Image Sensor MT9M001C12STM (Monochrome) Datasheet, Rev. M For the latest datasheet, please visit www.onsemi.com Features Table 1: Key Performance Parameters Parameter Value • Array Format (5:4): 1,280H x 1,024V (1,310,720 active Optical format 1/2-inch (5:4) pixels). Total (incl. dark pixels): 1,312H x 1,048V Active imager size 6.66 mm (H) x 5.32 mm (V) (1,374,976 pixels) • Frame Rate: 30 fps progressive scan; programmable Active pixels 1,280 H x 1,024 V • Shutter: Electronic Rolling Shutter (ERS) Pixel size 5.2 m x 5.2 m • Window Size: SXGA; programmable to any smaller Shutter type Electronic rolling shutter (ERS) Maximum data rate/ format (VGA, QVGA, CIF, QCIF, etc.) 48 MPS/48 MHz • Programmable Controls: Gain, frame rate, frame size master clock Frame SXGA 30 fps progressive scan; rate (1280 x 1024) programmable Applications ADC resolution 10-bit, on-chip Responsivity 2.1 V/lux-sec • Digital still cameras Dynamic range 68.2 dB • Digital video cameras •PC cameras SNRMAX 45 dB Supply voltage 3.0 V3.6 V, 3.3 V nominal 363 mW at 3.3 V (operating); Power consumption General Description 294 W (standby) Operating temperature 0°C to +70°C The ON Semiconductor MT9M001 is an SXGA-format with a 1/2-inch CMOS active-pixel digital image sen- Packaging 48-pin CLCC sor. The active imaging pixel array of 1,280H x 1,024V. It The sensor can be operated in its default mode or pro- incorporates sophisticated camera functions on-chip grammed by the user for frame size, exposure, gain set- such as windowing, column and row skip mode, and ting, and other parameters. -
More About Digital Cameras Image Characteristics Several Important
More about Digital Cameras Image Characteristics Several important characteristics of digital images include: Physical Size How big is the image that has been captured, as measured in inches or pixels? File Size How large is the computer file that makes up the image, as measured in kilobytes or megabytes? Pixels All digital images taken with a digital camera are made up of pixels (short for picture elements). A pixel is the smallest part (sometimes called a point or a dot) of a digital image and the total number of pixels make up the image and help determine its size and its resolution, or how much information is included in the image when we view it. Generally speaking, the larger the number of pixels an image contains, the sharper it will appear, especially when it is enlarged, which is what happens when we want to print our photographs larger than will fit into small 3 1\2 X 5 inch or 5 X 7 inch frames. You will notice in the first picture below that the Grand Canyon is in sharp focus and there is a large amount of detail in the image. However, when the image is enlarged to an extreme level, the individual pixels that make up the image are visible--and the image is no longer clear and sharp. Megapixels The term megapixels means one million pixels. When we discuss how sharp a digital image is or how much resolution it has, we usually refer to the number of megapixels that make up the image. One of the biggest selling features of digital cameras is the number of megapixels it is capable of producing when a picture is taken. -
Oxnard Course Outline
Course ID: DMS R120B Curriculum Committee Approval Date: 04/25/2018 Catalog Start Date: Fall 2018 COURSE OUTLINE OXNARD COLLEGE I. Course Identification and Justification: A. Proposed course id: DMS R120B Banner title: AdobePhotoShop II Full title: Adobe PhotoShop II B. Reason(s) course is offered: This course provides the development of skills necessary to combine the use of Photoshop digital image editing software with Adobe LightRoom's expanded digital photographic image editing abilities. These skills will enhance a student’s ability to enter into employment positions such as web master, graphics design, and digital image processing. C. C-ID: 1. C-ID Descriptor: 2. C-ID Status: D. Co-listed as: Current: None II. Catalog Information: A. Units: Current: 3.00 B. Course Hours: 1. In-Class Contact Hours: Lecture: 43.75 Activity: 0 Lab: 26.25 2. Total In-Class Contact Hours: 70 3. Total Outside-of-Class Hours: 87.5 4. Total Student Learning Hours: 157.5 C. Prerequisites, Corequisites, Advisories, and Limitations on Enrollment: 1. Prerequisites Current: DMS R120A: Adobe Photoshop I 2. Corequisites Current: 3. Advisories: Current: 4. Limitations on Enrollment: Current: D. Catalog description: Current: This course will continue the development of students’ skills in the use of Adobe Photoshop digital image editing software by integrating the enhanced editing capabilities of Adobe Lightroom into the Adobe Photoshop workflow. Students will learn how to “punch up” colors in specific areas of digital photographs, how to make dull-looking shots vibrant, remove distracting objects, straighten skewed shots and how to use Photoshop and Lightroom to create panoramas, edit Adobe raw DNG photos on mobile device, and apply Boundary Wrap to a merged panorama to prevent loss of detail in the image among other skills. -
Implementation of RGB and Grayscale Images in Plant Leaves Disease Detection – Comparative Study
ISSN (Print) : 0974-6846 Indian Journal of Science and Technology, Vol 9(6), DOI: 10.17485/ijst/2016/v9i6/77739, February 2016 ISSN (Online) : 0974-5645 Implementation of RGB and Grayscale Images in Plant Leaves Disease Detection – Comparative Study K. Padmavathi1* and K. Thangadurai2 1Research and Development Centre, Bharathiar University, Coimbatore - 641046, Tamil Nadu, India; [email protected] 2PG and Research Department of Computer Science, Government Arts College (Autonomous), Karur - 639007, Tamil Nadu, India; [email protected] Abstract Background/Objectives: Digital image processing is used various fields for analyzing differentMethods/Statistical applications such as Analysis: medical sciences, biological sciences. Various image types have been used to detect plant diseases. This work is analyzed and compared two types of images such as Grayscale, RGBResults/Finding: images and the comparative result is given. We examined and analyzed the Grayscale and RGB images using image techniques such as pre processing, segmentation, clustering for detecting leaves diseases, In detecting the infected leaves, color becomes an level.important Conclusion: feature to identify the disease intensity. We have considered Grayscale and RGB images and used median filter for image enhancement and segmentation for extraction of the diseased portion which are used to identify the disease RGB image has given better clarity and noise free image which is suitable for infected leaf detection than GrayscaleKeywords: image. Comparison, Grayscale Images, Image Processing, Plant Leaves Disease Detection, RGB Images, Segmentation 1. Introduction 2. Grayscale Image: Grayscale image is a monochrome image or one-color image. It contains brightness Images are the most important data for analysation of information only and no color information. -
12. Optical Data Storage
12. Optical data storage 12.1 Theory of color RGB additive color model B B Blue Magenta (0.6, 0.2, 1) (0,0,1) (1,0,1) Gray shades Cyan (g,g,g) (0,1,1) White (1,1,1) (0.6, 1, 0.7) R R Black Red (0,0,0) (1,0,0) G Green (0,1,0) (1, 0.7, 0.1) Yellow (1,1,0) G The RGB color model converts the additive color mixing system into a digital system, in which any color is represented by a point in a color tridimensional space and thus by a three coordinates vector. The coordinates are composed of the respective proportions of the primary light colors (Red, Green, Blue). The RGB model is often used by software as it requires no conversion in order to display colors on a computer screen. 169 CMY subtractive color model Y Y Yellow Green (0, 0.3, 0.9) (0,0,1) (1,0,1) Gray shades Red (g,g,g) (0,1,1) Black (1,1,1) C (0.4, 0, 0.3) C White Cyan (0,0,0) (1,0,0) M Magenta (0,1,0) (0.4, 0.8, 0) Blue (1,1,0) M The CMY color model corresponds to the subtractive color mixing system. The coordinates of a point in this color space are composed of the respective proportions of the primary filter colors (Cyan, Magenta, Yellow). The CMY model is used whenever a colored document is printed. The conversion RGB and CMY models can be written as follows: For the orange point, for example: 170 HSV color model V Green Yellow 120° 60° (37°, 0.9, 1) White H 1.0 S Cyan Red 180° 0° V Blue Magenta 240° 300° Hue is given in polar coordinate. -
14. Color Mapping
14. Color Mapping Jacobs University Visualization and Computer Graphics Lab Recall: RGB color model Jacobs University Visualization and Computer Graphics Lab Data Analytics 691 CMY color model • The CMY color model is related to the RGB color model. •Itsbasecolorsare –cyan(C) –magenta(M) –yellow(Y) • They are arranged in a 3D Cartesian coordinate system. • The scheme is subtractive. Jacobs University Visualization and Computer Graphics Lab Data Analytics 692 Subtractive color scheme • CMY color model is subtractive, i.e., adding colors makes the resulting color darker. • Application: color printers. • As it only works perfectly in theory, typically a black cartridge is added in practice (CMYK color model). Jacobs University Visualization and Computer Graphics Lab Data Analytics 693 CMY color cube • All colors c that can be generated are represented by the unit cube in the 3D Cartesian coordinate system. magenta blue red black grey white cyan yellow green Jacobs University Visualization and Computer Graphics Lab Data Analytics 694 CMY color cube Jacobs University Visualization and Computer Graphics Lab Data Analytics 695 CMY color model Jacobs University Visualization and Computer Graphics Lab Data Analytics 696 CMYK color model Jacobs University Visualization and Computer Graphics Lab Data Analytics 697 Conversion • RGB -> CMY: • CMY -> RGB: Jacobs University Visualization and Computer Graphics Lab Data Analytics 698 Conversion • CMY -> CMYK: • CMYK -> CMY: Jacobs University Visualization and Computer Graphics Lab Data Analytics 699 HSV color model • While RGB and CMY color models have their application in hardware implementations, the HSV color model is based on properties of human perception. • Its application is for human interfaces. Jacobs University Visualization and Computer Graphics Lab Data Analytics 700 HSV color model The HSV color model also consists of 3 channels: • H: When perceiving a color, we perceive the dominant wavelength. -
Use and Analysis of Color Models in Image Processing
cess Pro ing d & o o T F e c f h o Sharma and Nayyer, J Food Process Technol 2015, 7:1 n l o a l n o DOI: 10.4172/2157-7110.1000533 r Journal of Food g u y o J Processing & Technology ISSN: 2157-7110 Perspective Open Access Use and Analysis of Color Models in Image Processing Bhubneshwar Sharma* and Rupali Nayyer Department of Electronics and Communication Engineering, S.S.C.E.T, Punjab Technical University, India Abstract The use of color image processing is divided by two factors. First color is used in object identification and simplifies extraction from a scene and color is powerful descriptor. Second, humans can use thousands of color shades and intensities. Color Image Processing is divided into two areas full color and pseudo-color processing. In this processing various color models are used that are based on color recognition, color components etc. A few papers on various applications such as lane detection, face detection, fruit quality evaluation etc based on these color models have been published. A survey on widely used models RGB, HSI, HSV, RGI, etc. is represented in this paper. Keywords: Image processing; Color models; RGB; HIS; HSV; RGI for matter surfaces, while ignoring ambient light, normalized RGB is invariant (under certain assumptions) to changes of surface orientation Introduction relatively to the light source. This, together with the transformation An image may be defined as a two dimensional function, f(x, y), simplicity helped this color space to gain popularity among the where x and y are spatial coordinates and the amplitude of f at any researchers [5-7] (Figure 1).