Lec07, Image I (Acquisition and Representation), v1.07m.ppt

Multimedia SystemsMultimedia Systems

Image IImage I

(Acquisition and Representation)(Acquisition and Representation)

Course PresentationCourse Presentation

(Acquisition and Representation)(Acquisition and Representation)

Mahdi Amiri

March 2014

Sharif University of Technology

Image RepresentationColor DepthColor Depth

2bit2bit 8bit8bitThe number of bits used The number of bits used

to represent the color of to represent the color of

a single pixel.a single pixel.

bits per pixel (bits per pixel (bppbpp).).

1bit: Monochrome1bit: Monochrome

Page 1 Multimedia Systems, Mahdi Amiri, Image I

1bit1bit 4bit4bit 24bit24bit

1bit: Monochrome1bit: Monochrome

24bit: 24bit: TruecolorTruecolor

Image RepresentationIndexed Color, PaletteIndexed Color, Palette

It is a form of vector quantization compression.

A 2A 2--bit indexed bit indexed

color image color image

Page 2

Color tableColor table

(the palette)(the palette)

88--bit (256bit (256--color) color)

Indexed image and Indexed image and

its own paletteits own palette

88--bit Grayscale bit Grayscale

image and paletteimage and palette

Multimedia Systems, Mahdi Amiri, Image I

Image Representation, PaletteDisadvantagesDisadvantages

Limited set of simultaneous colors per image.

If the original color palette for a given indexed image is

lost, it can be nearly impossible to restore it.

Page 3

44--bitbit88--bitbit2424--bitbit Incorrect Incorrect

palettepalette


Image RepresentationHalftoneHalftone

A technique that simulates continuous tone imagery through the use

of dots, varying either in size, in shape or in spacing.

Color

Color

Page 4

How the human eye How the human eye

would see this sort of would see this sort of

arrangement from a arrangement from a

sufficient sufficient distancedistance..

Halftone Halftone

dotsdots

Three examples of Three examples of color color halftoninghalftoning with CMYK separations. From left to with CMYK separations. From left to

right: The cyan separation, the magenta separation, the yellow separation, the right: The cyan separation, the magenta separation, the yellow separation, the

black separation, the combined halftone pattern and finally how the human eye black separation, the combined halftone pattern and finally how the human eye

would observe the combined halftone pattern from a sufficient distance.would observe the combined halftone pattern from a sufficient distance.

Color

Color H

alftoning

Halfto

ning


Image Representation, DitheringDefinitionDefinition

An intentionally applied form of noise used to randomize

quantization error.

Etymology: …Mechanical computers performed more accurately

when flying on board the aircraft, and less well on ground!

Application: Increasing color depth without adding new bits

Page 5

Application: Increasing color depth without adding new bits

11--bitbit

black and white black and white thresholdingthresholding

2424--bitbit 11--bit,bit,

with Floydwith Floyd--Steinberg ditheringSteinberg dithering


Image Representation, DitheringFloydFloyd––Steinberg AlgorithmSteinberg Algorithm

Distribute the quantization residual to neighboring

pixels that have not yet been processed.

Pseudocode:

Page 6

for each y from top to bottom

for each x from left to right

oldpixel := pixel[x][y]

newpixel := find_closest_palette_color(oldpixel)

pixel[x][y] := newpixel

quant_error := oldpixel – newpixel

pixel[x+1][y] := pixel[x+1][y] + 7/16 * quant_error

pixel[x-1][y+1] := pixel[x-1][y+1] + 3/16 * quant_error

pixel[x][y+1] := pixel[x][y+1] + 5/16 * quant_error

pixel[x+1][y+1] := pixel[x+1][y+1] + 1/16 * quant_error

Distribution matrixDistribution matrix


Image Representation, DitheringColor Banding ArtifactColor Banding Artifact

Dithering prevents large-scale patterns such as "banding" in images.

Page 7

WebWeb--safe color palette safe color palette

with no ditheringwith no dithering

WebWeb--safe color palette safe color palette

with Floydwith Floyd––Steinberg Steinberg

ditheringdithering


Image ResolutionImage ResolutionImage Resolution

Image Resolution, Most Common Display ResolutionsImage Resolution, Most Common Display Resolutions

HD

Full-HD

Page 8

Aspect R

atioAspect R

atio


3rd gen. of

iPad (QXGA)Google's Nexus 10

(WQXGA)

Image ResolutionDigital TV ResolutionsDigital TV Resolutions

The term 4K refers to the

horizontal resolution of these

formats, which are all on the

order of 4,000 pixels.


en.wikipedia.org/wiki/4K_resolution

4K Ultra high definition television (UHD)

is a resolution of 3840 pixels × 2160 pixels

(8.3 megapixels, aspect ratio 16:9).

8K UHD which is 7680 pixels × 4320

pixels (33.2 megapixels).

16:9 resolutions in comparison.

Image ResolutionUHDTV, HDTD, SDTVUHDTV, HDTD, SDTV

SDTV: Standard-definition television.

HDTV: high-definition television.

UHDTV: Ultra HDTV.


http://en.wikipedia.org/wiki/Ultra_high_definition_television

Chart showing resolutions for 8K UHDTV, 4K

UHDTV, 1080p HDTV, and 480i SDTV.

Diagram of the CIE 1931 color space that shows the Rec. 2020

(UHDTV) color space in the outer triangle and Rec. 709 (HDTV)

color space in the inner triangle. Both Rec. 2020 and Rec. 709 use

Illuminant D65 for the white point.

Image ResolutionMegapixel (MP)Megapixel (MP)

One million pixels

To express:

The number of pixels in an image

The number of image sensor elements of digital cameras

The number of display elements of digital displays

8 MP Phone 8 MP Phone

CameraCamera

Page 11

The number of display elements of digital displays

2048×1536 sensor elements, or QXGA display

�� 3.1 MP (2048 × 1536 = 3,145,728)

160 MP 160 MP

CameraCamera


Image ResolutionPixels per inch (Pixels per inch (ppippi))

Pixels per inch (PPI) or pixel density is a measurement of the

resolution of devices in various contexts; typically computer displays,

image scanners, and digital camera image sensors.

Page 12

18 18 ppippi


72 72 ppippi 150 150 ppippi


The average human eye can only detect 300 ppi.

Page 13

iPhone 5/5s

4"

1136x640

326 ppi


List of displays by pixel density

http://en.wikipedia.org/wiki/List_of_displays_by_pixel_density

HTC One

4.7"

1920x1080

468 ppi

Galaxy Note 3

5.7"

1080x1920

388 ppi

iPad 4/Air

9.7"

2048x1536

264 ppi

Lumia 920

4.5"

1280x768

332 ppi

Xperia Z

5"

1920x1080

443 ppi


Pixels per inch for a

few more devices.

Page 14

iPhone 4, 4s

3.5"

640x960

326 ppi


iPad 1, iPad 2

9.7"

1024x768

132 ppi

Nokia N95

2.6"

240x320

153 ppi

Google Nexus One

3.7"

480x800

254 ppi

iPad 3

9.7"

2048x1536

264 ppi

Nokia Lumia 800

3.7"

800x480

252 ppi

Samsung I9100 Galaxy S II

4.27"

480x800

219 ppi

Galaxy Note II

5.55"

720x1280

267 ppi

Image VisionHistogramHistogram

Plots the number of pixels for each tonal value. By looking at the histogram for a specific

image a viewer will be able to judge the entire tonal distribution at a glance.

Count (Number of

pixels for each

different intensity

value)

Image

histogram


Intensity (tonal value)

Image VisionContrastContrast

Contrast is the difference in visual properties that Contrast is the difference in visual properties that

makes an object distinguishable from other objects makes an object distinguishable from other objects

and the background.and the background.

FormulaFormula


Typ. histogram ofTyp. histogram of

a low contrast imagea low contrast image

Typ. histogram ofTyp. histogram of

a high contrast imagea high contrast image

Image VisionHistogram EqualizationHistogram Equalization

Histogram equalization is a method in image processing of Histogram equalization is a method in image processing of

contrast adjustment using the image's histogram.contrast adjustment using the image's histogram.


Image Vision Histogram equalization, ExampleHistogram equalization, Example


Image File FormatsRaster and Vector GraphicsRaster and Vector Graphics

Page 19

Raster Graphics (Bitmap)Raster Graphics (Bitmap)

.BMP, .JPG, .PNG, .GIF.BMP, .JPG, .PNG, .GIF

Vector GraphicsVector Graphics

.CGM, .SVG.CGM, .SVGBothBoth

.AI, .CDR, .PSD, .TIFF.AI, .CDR, .PSD, .TIFF


Image RepresentationPanoramaPanorama

Example Software:Example Software:

““HuginHugin” and “” and “AutoStitchAutoStitch””

Page 20

Stitching images captured Stitching images captured

above above MiladMilad TowerTower


Image RepresentationAutoStitchAutoStitch ProcessProcess

Page 21

Example algorithm: SIFT Example algorithm: SIFT KeypointKeypoint detection and matching.detection and matching.


Image RepresentationLarge Screen ProjectionLarge Screen Projection


Image AcquisitionHighHigh--DynamicDynamic--Range (HDR)Range (HDR)

Page 23

HDR, Accurately HDR, Accurately

representing the range representing the range

of intensity levels of intensity levels

found in real scenesfound in real scenes

4 Images captured with 4 Images captured with

different Exposure different Exposure

Values (EV or stop)Values (EV or stop)


Image AcquisitionHDR Movie DemoHDR Movie Demo

Page 24

Play HDR movie demoPlay HDR movie demo


Image Acquisition, HDRAlgorithm: Tone MappingAlgorithm: Tone Mapping

Tone mapped HDR imageTone mapped HDR image

This isThis is

Exposure BracketingExposure Bracketing

Page 25

To overcome the limited dynamic To overcome the limited dynamic

range of current standard digital range of current standard digital

imaging techniquesimaging techniques

A simple version of tone mapping: A simple version of tone mapping:

Mean Value MappingMean Value Mapping


Image AcquisitionFocus BracketingFocus Bracketing

Bracketing is the general technique of taking several shots of the same subject using different camera settings.

Page 26

Focus stacked image Focus stacked image

A sequence of 5 incrementally focused imagesA sequence of 5 incrementally focused images

Example Software: Example Software:

““CombineZPCombineZP””


Image AcquisitionFocus BracketingFocus Bracketing

Example Application:Example Application:

MicroscopyMicroscopy

Page 27

The resulting focus stacked image with an The resulting focus stacked image with an

extended depth of fieldextended depth of field

The three source image The three source image

slices at three focal depthsslices at three focal depths

Contributions in the final Contributions in the final

"focus stacked" image"focus stacked" image


Thank You

Multimedia SystemsMultimedia Systems

Image IImage I

Page 28

Thank You

1. http://ce.sharif.edu/~m_amiri/

2. http://www.dml.ir/

FIND OUT MORE AT...

Next Session: Image IINext Session: Image II


Lec07, Image I (Acquisition and Representation), v1.07m.ppt

Documents