Human Skin Detection Using RGB, HSV and Ycbcr Color Models
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Color Management
Color Management hotoshop 5.0 was justifiably praised as a ground- breaking upgrade when it was released in the summer of 1998, although the changes made to the color P management setup were less well received in some quarters. This was because the revised system was perceived to be complex and unnecessary. Bruce Fraser once said of the Photoshop 5.0 color management system ‘it’s push-button simple, as long as you know which of the 60 or so buttons to push!’ Attitudes have changed since then (as has the interface) and it is fair to say that most people working today in the pre-press industry are now using ICC color profile managed workflows. The aim of this chapter is to introduce the basic concepts of color management before looking at the color management interface in Photoshop and the various color management settings. 1 Color management Adobe Photoshop CS6 for Photographers: www.photoshopforphotographers.com The need for color management An advertising agency art buyer was once invited to address a meeting of photographers. The chair, Mike Laye, suggested we could ask him anything we wanted, except ‘Would you like to see my book?’ And if he had already seen your book, we couldn’t ask him why he hadn’t called it back in again. And if he had called it in again we were not allowed to ask why we didn’t get the job. And finally, if we did get the job we were absolutely forbidden to ask why the color in the printed ad looked nothing like the original photograph! That in a nutshell is a problem which has bugged many of us throughout our working lives, and it is one which will be familiar to anyone who has ever experienced the difficulty of matching colors on a computer display with the original or a printed output. -
Color Models
Color Models Jian Huang CS456 Main Color Spaces • CIE XYZ, xyY • RGB, CMYK • HSV (Munsell, HSL, IHS) • Lab, UVW, YUV, YCrCb, Luv, Differences in Color Spaces • What is the use? For display, editing, computation, compression, …? • Several key (very often conflicting) features may be sought after: – Additive (RGB) or subtractive (CMYK) – Separation of luminance and chromaticity – Equal distance between colors are equally perceivable CIE Standard • CIE: International Commission on Illumination (Comission Internationale de l’Eclairage). • Human perception based standard (1931), established with color matching experiment • Standard observer: a composite of a group of 15 to 20 people CIE Experiment CIE Experiment Result • Three pure light source: R = 700 nm, G = 546 nm, B = 436 nm. CIE Color Space • 3 hypothetical light sources, X, Y, and Z, which yield positive matching curves • Y: roughly corresponds to luminous efficiency characteristic of human eye CIE Color Space CIE xyY Space • Irregular 3D volume shape is difficult to understand • Chromaticity diagram (the same color of the varying intensity, Y, should all end up at the same point) Color Gamut • The range of color representation of a display device RGB (monitors) • The de facto standard The RGB Cube • RGB color space is perceptually non-linear • RGB space is a subset of the colors human can perceive • Con: what is ‘bloody red’ in RGB? CMY(K): printing • Cyan, Magenta, Yellow (Black) – CMY(K) • A subtractive color model dye color absorbs reflects cyan red blue and green magenta green blue and red yellow blue red and green black all none RGB and CMY • Converting between RGB and CMY RGB and CMY HSV • This color model is based on polar coordinates, not Cartesian coordinates. -
An Improved SPSIM Index for Image Quality Assessment
S S symmetry Article An Improved SPSIM Index for Image Quality Assessment Mariusz Frackiewicz * , Grzegorz Szolc and Henryk Palus Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; [email protected] (G.S.); [email protected] (H.P.) * Correspondence: [email protected]; Tel.: +48-32-2371066 Abstract: Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications. Keywords: image quality assessment; image databases; superpixels; color image; color space; image quality measures Citation: Frackiewicz, M.; Szolc, G.; Palus, H. An Improved SPSIM Index 1. Introduction for Image Quality Assessment. Quantitative domination of acquired color images over gray level images results in Symmetry 2021, 13, 518. https:// the development not only of color image processing methods but also of Image Quality doi.org/10.3390/sym13030518 Assessment (IQA) methods. -
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. -
COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING
COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING Color space A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components (e.g. RGB and CMYK are color models). However, a color model with no associated mapping function to an absolute color space is a more or less arbitrary color system with little connection to the requirements of any given application. Adding a certain mapping function between the color model and a certain reference color space results in a definite "footprint" within the reference color space. This "footprint" is known as a gamut, and, in combination with the color model, defines a new color space. For example, Adobe RGB and sRGB are two different absolute color spaces, both based on the RGB model. In the most generic sense of the definition above, color spaces can be defined without the use of a color model. These spaces, such as Pantone, are in effect a given set of names or numbers which are defined by the existence of a corresponding set of physical color swatches. This article focuses on the mathematical model concept. Understanding the concept Most people have heard that a wide range of colors can be created by the primary colors red, blue, and yellow, if working with paints. Those colors then define a color space. We can specify the amount of red color as the X axis, the amount of blue as the Y axis, and the amount of yellow as the Z axis, giving us a three-dimensional space, wherein every possible color has a unique position. -
Cielab Color Space
Gernot Hoffmann CIELab Color Space Contents . Introduction 2 2. Formulas 4 3. Primaries and Matrices 0 4. Gamut Restrictions and Tests 5. Inverse Gamma Correction 2 6. CIE L*=50 3 7. NTSC L*=50 4 8. sRGB L*=/0/.../90/99 5 9. AdobeRGB L*=0/.../90 26 0. ProPhotoRGB L*=0/.../90 35 . 3D Views 44 2. Linear and Standard Nonlinear CIELab 47 3. Human Gamut in CIELab 48 4. Low Chromaticity 49 5. sRGB L*=50 with RGB Numbers 50 6. PostScript Kernels 5 7. Mapping CIELab to xyY 56 8. Number of Different Colors 59 9. HLS-Hue for sRGB in CIELab 60 20. References 62 1.1 Introduction CIE XYZ is an absolute color space (not device dependent). Each visible color has non-negative coordinates X,Y,Z. CIE xyY, the horseshoe diagram as shown below, is a perspective projection of XYZ coordinates onto a plane xy. The luminance is missing. CIELab is a nonlinear transformation of XYZ into coordinates L*,a*,b*. The gamut for any RGB color system is a triangle in the CIE xyY chromaticity diagram, here shown for the CIE primaries, the NTSC primaries, the Rec.709 primaries (which are also valid for sRGB and therefore for many PC monitors) and the non-physical working space ProPhotoRGB. The white points are individually defined for the color spaces. The CIELab color space was intended for equal perceptual differences for equal chan- ges in the coordinates L*,a* and b*. Color differences deltaE are defined as Euclidian distances in CIELab. This document shows color charts in CIELab for several RGB color spaces. -
Foveon FO18-50-F19 4.5 MP X3 Direct Image Sensor
® Foveon FO18-50-F19 4.5 MP X3 Direct Image Sensor Features The Foveon FO18-50-F19 is a 1/1.8-inch CMOS direct image sensor that incorporates breakthrough Foveon X3 technology. Foveon X3 direct image sensors Foveon X3® Technology • A stack of three pixels captures superior color capture full-measured color images through a unique stacked pixel sensor design. fidelity by measuring full color at every point By capturing full-measured color images, the need for color interpolation and in the captured image. artifact-reducing blur filters is eliminated. The Foveon FO18-50-F19 features the • Images have improved sharpness and immunity to color artifacts (moiré). powerful VPS (Variable Pixel Size) capability. VPS provides the on-chip capabil- • Foveon X3 technology directly converts light ity of grouping neighboring pixels together to form larger pixels that are optimal of all colors into useful signal information at every point in the captured image—no light for high frame rate, reduced noise, or dual mode still/video applications. Other absorbing filters are used to block out light. advanced features include: low fixed pattern noise, ultra-low power consumption, Variable Pixel Size (VPS) Capability and integrated digital control. • Neighboring pixels can be grouped together on-chip to obtain the effect of a larger pixel. • Enables flexible video capture at a variety of resolutions. • Enables higher ISO mode at lower resolutions. Analog Biases Row Row Digital Supplies • Reduces noise by combining pixels. Pixel Array Analog Supplies Readout Reset 1440 columns x 1088 rows x 3 layers On-Chip A/D Conversion Control Control • Integrated 12-bit A/D converter running at up to 40 MHz. -
NEC Multisync® PA311D Wide Gamut Color Critical Display Designed for Photography and Video Production
NEC MultiSync® PA311D Wide gamut color critical display designed for photography and video production 1419058943 High resolution and incredible, predictable color accuracy. The 31” MultiSync PA311D is the ultimate desktop display for applications where precise color is essential. The innovative wide-gamut LED backlight provides 100% coverage of Adobe RGB color space and 98% coverage of DCI-P3, enabling more accurate colors to be displayed on screen. Utilizing a high performance IPS LCD panel and backed by a 4 year warranty with Advanced Exchange, the MultiSync PA311D delivers high quality, accurate images simply and beautifully. Impeccable Image Performance The wide-gamut LED LCD backlight combined with NEC’s exclusive SpectraView Engine deliver precise color in every environment. • True 4K resolution (4096 x 2160) offers a high pixel density • Up to 100% coverage of Adobe RGB color space and 98% coverage of DCI-P3 • 10-bit HDMI and DisplayPort inputs display up to 1.07 billion colors out of a palette of 4.3 trillion colors Ultimate Color Management The sophisticated SpectraView Engine provides extensive, intuitive control over color settings. • MultiProfiler software and on-screen controls provide access to thousands of color gamut, gamma, white point, brightness and contrast combinations • Internal 14-bit 3D lookup tables (LUTs) work with optional SpectraViewII color calibration solution for unparalleled color accuracy A Perfect Fit for Your Workspace A Better Workflow Future-proof connectivity, great ergonomics, and VESA mount Exclusive, -
Single-Pixel Imaging Via Compressive Sampling
© DIGITAL VISION Single-Pixel Imaging via Compressive Sampling [Building simpler, smaller, and less-expensive digital cameras] Marco F. Duarte, umans are visual animals, and imaging sensors that extend our reach— [ cameras—have improved dramatically in recent times thanks to the intro- Mark A. Davenport, duction of CCD and CMOS digital technology. Consumer digital cameras in Dharmpal Takhar, the megapixel range are now ubiquitous thanks to the happy coincidence that the semiconductor material of choice for large-scale electronics inte- Jason N. Laska, Ting Sun, Hgration (silicon) also happens to readily convert photons at visual wavelengths into elec- Kevin F. Kelly, and trons. On the contrary, imaging at wavelengths where silicon is blind is considerably Richard G. Baraniuk more complicated, bulky, and expensive. Thus, for comparable resolution, a US$500 digi- ] tal camera for the visible becomes a US$50,000 camera for the infrared. In this article, we present a new approach to building simpler, smaller, and cheaper digital cameras that can operate efficiently across a much broader spectral range than conventional silicon-based cameras. Our approach fuses a new camera architecture Digital Object Identifier 10.1109/MSP.2007.914730 1053-5888/08/$25.00©2008IEEE IEEE SIGNAL PROCESSING MAGAZINE [83] MARCH 2008 based on a digital micromirror device (DMD—see “Spatial Light Our “single-pixel” CS camera architecture is basically an Modulators”) with the new mathematical theory and algorithms optical computer (comprising a DMD, two lenses, a single pho- of compressive sampling (CS—see “CS in a Nutshell”). ton detector, and an analog-to-digital (A/D) converter) that com- CS combines sampling and compression into a single non- putes random linear measurements of the scene under view. -
Image Processing
IMAGE PROCESSING ROBOTICS CLUB SUMMER CAMP’12 WHAT IS IMAGE PROCESSING? IMAGE PROCESSING = IMAGE + PROCESSING WHAT IS IMAGE? IMAGE = Made up of PIXELS Each Pixels is like an array of Numbers. Numbers determine colour of Pixel. TYPES OF IMAGES 1. BINARY IMAGE 2. GREYSCALE IMAGE 3. COLOURED IMAGE BINARY IMAGE Each Pixel has either 1 (White) or 0 (Black) Depth =1 (bit) Number of Channels = 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 GRAYSCALE Each Pixel has a value from 0 to 255. 0 : black and 1 : White Between 0 and 255 are shades of b&w. Depth=8 (bits) Number of Channels =1 GRAYSCALE IMAGE RGB IMAGE Each Pixel stores 3 values :- R : 0- 255 G: 0 -255 B : 0-255 Depth=8 (bits) Number of Channels = 3 RGB IMAGE HSV IMAGE Each pixel stores 3 values :- H ( hue ) : 0 -180 S (saturation) : 0-255 V (value) : 0-255 Depth = 8 (bits) Number of Channels = 3 Note : Hue in general is from 0-360 , but as hue is 8 bits in OpenCV , it is shrinked to 180 STARTING WITH OPENCV OpenCV is a library for C language developed for Image Processing To embed opencv library in Dev C complier , follow instructions in :- http://opencv.willowgarage.com/wiki/DevCpp HEADER FILES IN C After embedding openCV library in Dev C include following header files:- #include "cv.h" #include "highgui.h" IMAGE POINTER An image is stored as a structure IplImage with following elements :- int height Width int width int nChannels int depth Height char *imageData int widthStep …. -
Creating 4K/UHD Content Poster
Creating 4K/UHD Content Colorimetry Image Format / SMPTE Standards Figure A2. Using a Table B1: SMPTE Standards The television color specification is based on standards defined by the CIE (Commission 100% color bar signal Square Division separates the image into quad links for distribution. to show conversion Internationale de L’Éclairage) in 1931. The CIE specified an idealized set of primary XYZ SMPTE Standards of RGB levels from UHDTV 1: 3840x2160 (4x1920x1080) tristimulus values. This set is a group of all-positive values converted from R’G’B’ where 700 mv (100%) to ST 125 SDTV Component Video Signal Coding for 4:4:4 and 4:2:2 for 13.5 MHz and 18 MHz Systems 0mv (0%) for each ST 240 Television – 1125-Line High-Definition Production Systems – Signal Parameters Y is proportional to the luminance of the additive mix. This specification is used as the color component with a color bar split ST 259 Television – SDTV Digital Signal/Data – Serial Digital Interface basis for color within 4K/UHDTV1 that supports both ITU-R BT.709 and BT2020. 2020 field BT.2020 and ST 272 Television – Formatting AES/EBU Audio and Auxiliary Data into Digital Video Ancillary Data Space BT.709 test signal. ST 274 Television – 1920 x 1080 Image Sample Structure, Digital Representation and Digital Timing Reference Sequences for The WFM8300 was Table A1: Illuminant (Ill.) Value Multiple Picture Rates 709 configured for Source X / Y BT.709 colorimetry ST 296 1280 x 720 Progressive Image 4:2:2 and 4:4:4 Sample Structure – Analog & Digital Representation & Analog Interface as shown in the video ST 299-0/1/2 24-Bit Digital Audio Format for SMPTE Bit-Serial Interfaces at 1.5 Gb/s and 3 Gb/s – Document Suite Illuminant A: Tungsten Filament Lamp, 2854°K x = 0.4476 y = 0.4075 session display. -
Preparing Images for Delivery
TECHNICAL PAPER Preparing Images for Delivery TABLE OF CONTENTS So, you’ve done a great job for your client. You’ve created a nice image that you both 2 How to prepare RGB files for CMYK agree meets the requirements of the layout. Now what do you do? You deliver it (so you 4 Soft proofing and gamut warning can bill it!). But, in this digital age, how you prepare an image for delivery can make or 13 Final image sizing break the final reproduction. Guess who will get the blame if the image’s reproduction is less than satisfactory? Do you even need to guess? 15 Image sharpening 19 Converting to CMYK What should photographers do to ensure that their images reproduce well in print? 21 What about providing RGB files? Take some precautions and learn the lingo so you can communicate, because a lack of crystal-clear communication is at the root of most every problem on press. 24 The proof 26 Marking your territory It should be no surprise that knowing what the client needs is a requirement of pro- 27 File formats for delivery fessional photographers. But does that mean a photographer in the digital age must become a prepress expert? Kind of—if only to know exactly what to supply your clients. 32 Check list for file delivery 32 Additional resources There are two perfectly legitimate approaches to the problem of supplying digital files for reproduction. One approach is to supply RGB files, and the other is to take responsibility for supplying CMYK files. Either approach is valid, each with positives and negatives.