电子科技大学多媒体通信第三章

1、Graphics and Image Data RepresentationsContentsnRGB Color Model in ImagesnGraphics and ImagenYUV Color ModelnColor Science3 Primary Colorsn3 Primary Colors nRed, Green, BluenSecondary Colors: the primary color can be added to producenYellow = Red GreennMagenta(品红) = Red BluenCyan(青色) = Green BluenTr

2、ichromatic theory: Certain wavelengths of red, green, and blue, when combined with each other, in various proportion will produce a wider rang of color. Triangular Dot PatternsTriangular Dot PatternsRGB12552550Yellow22550255Magenta30255255CyanColor TheorynMixtures of light (additive primaries)nMixtu

3、res of pigments (subtractive primaries)Primary and secondary colorsPigmentColor TheoryColor GenerationCRT( Cathode Ray Tube )HDTV CRTColor GenerationLCD (Liquid Crystal Display )PDP (Plasma Display Panel)DLP (Digital Light Procession)DLP (Digital Light Procession)Graphics and Image Data TypesnWhat i

4、s an image? nA spatial representation of an object, a 2D or 3D scene or another image.nImage: a function of light intensity valueBasic ConceptnDigital Image is represented by : na of numeric values each representing a . nComputer operation nSampling at discrete intervalsnQuantizing intensity valueOp

5、tic or Photographic sensorsSamplingQuantizationSceneDigital ImageAnalog ImageSampled at discrete intervals ()Intensity todiscrete valuesDigitization of a continuous image nExample: the figure has been divided into N = 16 rows and M = 16 columns. The value assigned to every pixel is the average inten

6、sity in the pixel rounded to the nearest integer value nRectangular sampling nhexagonal samplingDigital Image RepresentationnDigital Image: A matrix form I (r, c)nI： Intensity valuegrey scale level or color (color encoding)nr： row nc：columnnPixel (picture element): the points at which an image is sa

7、mpledSpatial ResolutionDigital Image RepresentationNotions: Resolution: MN Pixel : picture element I(r,c)-2-D matrix: Represents the intensity value at the position corresponding to row r and column c of the matrixDigital Image RepresentationnExample of digital image RepresentationnSpatial resolutio

8、n = 3434 pixelnSample value: Grey scale level: 0 or 255Digital Image RepresentationnSquare Sampling GridnDistance between grid points: determine how much detail to be resolvednEqually spaced（等间隔）nResolution(分辨率) nRow Pixels Column PixelsnTo be determined by image system (成像系统)nPixel value: gray scal

9、e levels(灰度级别) or color nquantization Integer Digital Image RepresentationnColor Encoding (bits/pixel)nExample n2 intensity value: 1 bit (0/1)binary valued image (二值图)n8-bits (color or grayscale)n24-bits (color RGB): true color Red8bit Green8bit Blue8bitDigital Image RepresentationnCommon values of

10、digital image parameters ParameterTypical values NoteRows256,512,525,625,1024,1035 2k, k = 8, 9, 10Columns 256,512,768,1024,1320 Gray Levels 2,64,256,1024,4096,16384 2k, k = 1, 8, 24Images With Different Color EncodingnSpatial Resolution = row (pixel) columns (pixel)nImage Data = nSpatial Resolution

11、 Bits/pixel / 8 (Bytes)nSpatial Resolution log2GrayscaleLevels / 8nExample n1024 768 24/8=2,359,296 BytesnSample television picture (NTSC, 525 lines) with VGA (Video Graphics Array) video controller: 640 480, 8bit/pixel Data volume = spatial resolution bit/pixel = 640 480 8 2.457MbImage Data Computi

12、ngStored Image FormatnStoring 2-D array of values (2-D阵列阵列)nEach value represents the data associate with a pixel in the image.nThe value may be :n Binary bitmap a binary digit (0,1)nColor images: Red Green Blue intensities () R channel G channel B channel I (r, c) = IR+ IG+ IB, Red, green, blue com

13、ponents of the color at that pixel Indices to tables (palette) of an index to a table of color triples (). red, green and blue intensities () Stored Image FormatnStore an image in the conventional manner:nA collection of channels (value of each channel at each pixel)nOther information: nwidth, heigh

14、t, depth of image, nthe name of creatornExample nGIF (Graphical Interchange Format)nJEPG (Joint Photographic Expert Group)nTIFF (Tagged Image File format)nRIFF (Resource Interchange File Format)nIncluding: bitmap, vector drawing, video, animationAn Example: Bitmap File Structure1.BIT MAP FILE HEADER

15、 (14Bytes)2.BIT MAP INFO HEADER (40Bytes)3.Palette (color used in this image)4.Image Datan24-bits(truecolor): no palette, B/G/R components of each pixelnothers: indices to tables of B/G/R intensitiesAn Example: Bitmap File StructuredomainSize(Byte)CommentsBIT MAP FILE HEADERFID2 “BM”File Size 4Reser

16、ved 4Unit: pixel Reserved 4Unit: pixel An Example: Bitmap File StructureBIT MAP INFO HEADERBits Per Pixel 2Bits per pixel 1 - binary bitmap4 16-valued bitmap8 256-valued bitmap16 - 16bit high color bitmap24 - 24bit true color bitmap32 - 32bit enhanced true color bitmapCompression 20 no compression B

17、itmap Data Size 4PalettePalette 4cImage DataBitmap Data WIndex NO. or RGB valueRGB ComponentsRed/Green/Blue Components: True ColorIndices to Tables of Red/Green/Blue Intensities256 2021-7-1030HomeworknCreate a bitmap file(.bmp) by yourselfnList its format, including the following information at leas

18、t:File Size :Image Resolution：Bits per pixel: The data offset of first pixel:DIB Header Size:Palette:Pixel Data ( please give at least one row data)Compress your homework(using winzip or winrar) to a compressed file, named in format of “”, and send by email to the following address:Email: uestc_Dead

19、line: Next weekend.Graphics FormatsnGraphics image formats are specified through graphics primitives (图元) and their attributes (属性)nGraphics primitives: line, rectangle, circle, ellipse, three-dimensional objects(3D), etcnA graphics package determines which primitives are supported. nGraphics Attrib

20、utes: line style (solid/broken line, line width, color etc.) Graphics FormatsnPrimitives + AttributesHigh level image representationnGraphical image are not represented by a pixel matrixnNeed to be converted into lower level representation (e.g. pixel matrix) when being displayednExample (x,y)r11001

21、1101101011110011110101101110011Graphics FormatsnStructure: logical groupingnGraphic primitives nAttributesnOther information: image representation nA higher level of representationnAdvantage:nReduced datanEasier manipulationnDisadvantage:nGraphical pixelsGraphics vs. ImageElementsColor Representatio

22、nDisplayorderRequired Computing RateManipulationData AmountGraphicsGraphics primitivesAttributes for graphics primitivesGraphics primitives orderHigherEasylessImagepixelsColor for each pixelPixel orderLowerComplicatedmuchIntroduction to 3D Renderingn3D renderingnStereoscopic renderingLeft-vs. Right-

23、handed coordinate systemsnModels depend on coordinate systemsnConvertion between Left- vs Right-handed coordinates.nMesh, Animation3D ModellingnMesh(vertexes)nTexturingnAnimation3D Rendering PrinciplenTransformationnModel-worldnViewingn(world-View)nProjectionnView-ScreennOrthographic ProjnPerspectiv

24、e ProjnP=P*world*view*projction3D Rendering PipelinesTradeoff between CPUs and GPUs3D Vision ConceptsThe key problem is how to sending left image to left eye only and right to right only.Parallax Barrier ApplicationnA mask is placed over the LCD display which directs light from alternate pixel colum

25、ns to each eye. Lenticular Lens ApplicationAn array of cylindrical lenses directs light from alternate pixel columns to a defined viewing zone, allowing each eye to receive a different image at an optimum distance.ContentsnRGB Color Model in ImagesnGraphics and Image Data TypesnYUV Color ModelnColor

26、 ScienceColor spaceRGBnColor vision is achieved through three signals, proportional to the relative intensities of Red, Green and Blue light (RGB) in each portion of the scenenThe three signals are conveyed separately to the input terminals of the picture tube, nPicture tube: reproduces at each poin

27、t the relative intensities of red, green and blue discerned by the camera. nOther colors can be coded as a combination of these primary colors. Color spaceYUVnVideo signal transmission: color encoding use luminance (亮度) and two chrominance (色度)signals.nHuman perception is more sensitive to brightnes

28、s than any chrominance informationnThe luminance values can be coded using higher bandwidth than the chrominance values. nA more suitable coding distinguished between luminance and chrominance Data compression nR, G, B Y, U, V : data compression 4:2:2nYUV signalnPAL (Phase-Alternative Line) TV stand

29、ard nY = 0.299R + 0.587G + 0.114BnU = ( B Y ) 0.493nV = ( R Y )0.877Luminance an Chrominance0.2990.5870.1140.1690.3320.5000.50.4190.081YRUGVB Luminance an ChrominancenYIQ Signal nNTSC (National Television Systems Committee) TV standard nYUV versus YIQ0.2990.5870.1140.5960.2750.3210.2120.5230.311YRIG

30、QB 0.2990.5870.114 0.5960.2750.3210.2120.5230.311YRGBIRGBQRGBLuminance an ChrominancenYCrCb SignalnCCIR 601 digital video standard1281280500. 03313. 01687. 00813. 04187. 0500. 0114. 0578. 0299. 0BGRCbCrYRGBYUVY componentU componentV componentYUVContentsnRGB Color Model in ImagesnGraphics and Image D

31、ata TypesnYUV Color ModelnColor ScienceLight and SpectranSir Isaac Newtons experiments-White light contains all the colors of a rainbow.Light and SpectranSpectral power distribution of daylightSpectral Sensitivity of the Eye nThe overall sensitivity as a dashed line-this important curve is called th

32、e luminous-efficiency function.the sum of the response curves for Red, Green, and BlueCharacteristics of human visual system (HVS)nMore sensitive to edges and contours(轮廓)nExample: Match-band effect (马赫带效应)nMore sensitive to luminance than chrominancenMore sensitive to low frequency components than

33、high frequency componentsnContrast simultaneously (同时对比度)nError of human visionnParallax（视差）: 3-D perceptionExample nMatch-band effectnContrast simultaneouslyExamplenYUV ComponentsExample nError of human visionExample Example Example Example Homework 31.Ex.2 on Page 792.Given an image, the resolutio

34、n is 640480, please calculate image size for the following: (1) A binary image (2) A 256-valued image (3) A true-color image. Extension- Compute Image ProcessingSpatial domain methodsGray-level Histogram 0255A bimodal histogram 40 190nGray-level Histogram:nis a function showing, for each gray-level,

35、 the number of pixels in the image which has that gray level.nEnhancement may be achieved by modifying the histogram of a given image in a specified manner HistogramHistogram-modification Techniques1.Converting an image into black and whitenThresholding (阈值) 255 if f(x, y) t g(x, y)= 0 if f(x, y) t

36、gray level frequency of occurrence0255A bimodal histogram 40 190tHistogram-modification Techniques1.Converting an image into black and whitenThresholding (阈值) 255 if f(x, y) t g(x, y)= 0 if f(x, y) t f(x,y)g(x,y)Example of Thresholdg(x,y)Histogram-modification Techniques2.Adjusting contrast and brig

37、ht Histogram-modification Techniques( , ), 0( , )() ( , )( , ), ( , )(255) ( , ), ( , )255255cf x yf x yaadcf x yag x yc af x ybbadf x ybd bf x ybExample of Adjusting contrast and brightHistogram equalization (直方图均匀化) nProduce a uniform densitynEqual to the Cumulative Distribution Function of r nPro

38、duce an image whose gray level have a uniform density rrdwwp0)(Image enhancementnAdjusting the image histogram to improve image contrast Example: Image EnhancementSourceResultAdjusting contrast and bright Example: Image EnhancementSourceResultExample: Image EnhancementSourceResultImage Smoothing nSmoothing operations are used primarily for diminishing spurious effects (寄生) that may b