introduction to image file formats

7/31/2019 Introduction to Image File Formats

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Chapter 1:Introduction to ComputerVision and Image Processing


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Overview: Computer Imaging

Definition of computer imaging: Acquisition and processing of visual information by

computer.

Why is it important? Human primary sense is visual sense. Information can be conveyed well through images

(one picture worth a thousand words).

Computer is required because the amount of datato be processed is huge.


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Overview: Computer Imaging

Computer imaging can be divided into two

main categories: Computer Vision: applications of the output are for

use by a computer.

Image Processing: applications of the output are

for use by human. These two categories are not totally separate

and distinct.


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Computer Vision

Does not involve human in the visual loop.

One of the major topic within this field isimage analysis (Chapter 2).

Image analysis involves the examination of

image data to facilitate in solving a vision

problem.


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Computer Vision

Image analysis process involves two other

topics: Feature extraction: acquiring higher level image

info (shape and color)

Pattern classification: using higher level image

information to identify objects within image.


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Computer Vision

Most computer vision applications involve

tasks that: Are tedious for people to perform.

Require work in a hostile environment.

Require a high processing rate.

Require access and use of a large database ofinformation.


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Computer Vision

Examples of applications of computer vision:

Quality control (inspect circuit board). Hand-written character recognition.

Biometrics verification (fingerprint, retina, DNA,

signature, etc).

Satellite image processing. Skin tumor diagnosis.

And many, many others.


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Image Processing

Processed images are to be used by human.

Therefore, it requires some understanding on howthe human visual system operates.

Among the major topics are: Image restoration (Chapter 3).

Image enhancement (Chapter 4). Image compression (Chapter 5).


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Image Processing

Image restoration:

The process of taking an image with some know,or estimated degradation, and restoring it to its

original appearance.

Done by performing the reverse of the degradation

process to the image.

Examples: correcting distortion in the optical

system of a telescope.


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Image Processing

An Example of Image Restoration


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Image Processing

Image enhancement:

Improve an image visually by taking an advantageof human visual systems response.

Example: improve contrast, image sharpening, and

image smoothing.


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Image Processing

An Example of Image Enhancement


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Image Processing

Image compression:

Remove the amount of data required to representan image by:Removing unnecessary data that are visually

unnecessary.

Taking advantage of the redundancy that is inherent in

most images.

Example: JPEG, MPEG, etc.


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Computer Imaging Systems

Computer imaging systems comprises of

both hardware and software. The hardware components can be divided

into three subsystems: The computer

Image acquisition: camera, scanner, videorecorder.

Image display: monitor, printer, film, video player.


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The software is used for the following tasks:

Manipulate the image and perform any desiredprocessing on the image data.

Control the image acquisition and storage process.

The computer system may be a general-

purpose computer with a frame grabber orimage digitizer board in it.


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Frame grabber is a special purpose piece of

hardware that digitizes standard analog videosignal.

Digitization of analog video signal is

important because computers can only

process digital data.


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Digitization is done by sampling the analog

signal or instantaneously measuring thevoltage of the signal at fixed interval in time.

The value of the voltage at each instant is

converted into a number and stored.

The number represents the brightness of theimage at that point.


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The grabbed image is now a digital image

and can be accessed as a two dimensionalarray of data. Each data point is called apixel(picture element).

The following notation is used to express a

digital image: I(r,c) = the brightness of the image at point (r,c)

where r = row and c = column.


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The CVIPtools Software

CVIPtools software contains C functions toperform all the operations that are discussedin the text book.

It also comes with an application with GUIinterface that allows you to perform variousoperations on an image. No coding is needed.

Users may vary all the parameters.

Results can be observed in real time.


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The CVIPtools Software

It is available from:

The CD-ROM that comes with the book. http://www.ee.siue.edu/CVIPtools
http://www.ee.siue.edu/CVIPtoolshttp://www.ee.siue.edu/CVIPtools


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Human Visual Perception

Human perception encompasses both the

physiological and psychological aspects.We will focus more on physiological aspects,

which are more easily quantifiable and

hence, analyzed.


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Human Visual Perception

Why study visual perception?

Image processing algorithms are designed basedon how our visual system works.

In image compression, we need to know what

information is not perceptually important and can

be ignored.

In image enhancement, we need to know what

types of operations that are likely to improve an

image visually.


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The Human Visual System

The human visual system consists of two

primary components the eye and the brain,which are connected by the optic nerve. Eye receiving sensor (camera, scanner).

Brain information processing unit (computer

system). Optic nerve connection cable (physical wire).


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This is how human visual system works:

Light energy is focused by the lens of the eye intosensors and retina.

The sensors respond to the light by an

electrochemical reaction that sends an electrical

signal to the brain (through the optic nerve).

The brain uses the signals to create neurological

patterns that we perceive as images.


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The visible light is an electromagnetic wave

with wavelength range of about 380 to 825nanometers. However, response above 700 nanometers is

minimal.

We cannot see many parts of theelectromagnetic spectrum.


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The visible spectrum can be divided into

three bands: Blue (400 to 500 nm).

Green (500 to 600 nm).

Red (600 to 700 nm).

The sensors are distributed across retina.


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There are two types of sensors: rods and

cones.

Rods: For night vision.

See only brightness (gray level) and not color.

Distributed across retina. Medium and low level resolution.


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Cones:

For daylight vision. Sensitive to color.

Concentrated in the central region of eye.

High resolution capability (differentiate small

changes).


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Blind spot:

No sensors. Place for optic nerve.

We do not perceive it as a blind spot because the

brain fills in the missing visual information.

Why does an object should be in center fieldof vision in order to perceive it in fine detail? This is where the cones are concentrated.


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Cones have higher resolution than rods

because they have individual nerves tied to

each sensor.

Rods have multiple sensors tied to each

nerve.

Rods react even in low light but see only asingle spectral band. They cannot distinguish

color.


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There are three types of cones. Each

responding to different wavelengths of light

energy.

The colors that we perceive are the

combined result of the response of the three

cones.


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Spatial Frequency Resolution

To understand the concept of spatial

frequency, we must first understand the

concept ofresolution.

Resolution: the ability to separate two

adjacent pixels.

If we can see that two adjacent pixels as beingseparate, then we can say that we can resolve the

two.


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Spatial frequency: how rapidly the signal

changes in space.


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If we increase the frequency, the stripes get

closer until they finally blend together.


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The distance between eye and image also affects the

resolution. The farther the image, the worse the resolution.

Why is this important? The number of pixels per square inch on a display device

must be large enough for us to see an image as being

realistic. Otherwise we will end up seeing blocks of colors. There is an optimum distance between the viewer and the

display device.


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Limitations of visual system in resolution are

due to both optical and neural factor. We cannot resolve things smaller than the

individual sensor.

Lens has finite size, which limits the amount of light

it can gather.

Lens is slightly yellow (which progresses with age);

limits eyes response to certain wavelength of light.


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Spatial resolution is affected by the average

background brightness of the display.

In general, we have higher spatial resolution

at brighter levels.

The visual system has less spatial resolution

for color information that has been decoupledfrom the brightness information.


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Brightness Adaptation

The vision system responds to a wide range

of brightness levels.

The perceived brightness (subjective

brightness) is a logarithmic function of the

actual brightness.

However, it is limited by the dark threshold (toodark) and the glare limit (too bright).


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We cannot see across the entire range at any

one time.

But our system will adapt to existing light

condition.

The pupil varies its size to control the amount

of light coming into the eye.


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It has been experimentally determined that

we can detect only about 20 changes in

brightness in a small area within a complex

image.

However, for an entire image, about 100 gray

levels are necessary to create a realisticimage. Due to brightness adaptation of our visual system.


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If fewer gray levels are used, we will observe

false contours (bogus line).

This resulted from gradually changing light

intensity not being accurately presented.


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Image with 8 bits/pixel (256

gray levels no false contour)

Image with 3 bits/pixel (8 gray

levels contain false contour)


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This accentuates edges and helps us to

distinguish and separates objects within an

image.

Combined with our brightness adaptation

response, this allows us to see outlines even

in dimly lit areas.


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An illustration of the

Mach Band Effect.

Observe the edges

between the different

brightness.

The edges seem to

be a bit stand outcompared to the rest

of the image.


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Temporal Resolution

Related to how we respond to visual

information as a function of time. Useful when considering video and motion in

images.

Can be measured using flicker sensitivity.

Flicker sensitivity refers to our ability toobserve a flicker in a video signal displayed

on a monitor.


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Temporal Resolution


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Temporal Resolution

The cutoff frequency is about 50 hertz

(cycles per second). We will not perceive any flicker for a video signal

above 50Hz.

TV uses frequency around 60Hz.

The brighter the lighting, the more sensitivewe are to changes.


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Image Representation

Digital image I(r, c) is represented as a two-

dimensional array of data.

Each pixel value corresponds to the

brightness of the image at point (r, c).

This image model is for monochrome (one

color, or black and white) image data.


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Image Representation

Multiband images (color, multispectral) canbe modeled by a different I(r, c) function for

each separate band of brightnessinformation.

Types of images that will discuss: Binary

Gray-scale

Color

Multispectral


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Binary Images

Takes only two values: Black and white (0 and 1)

Requires 1 bit/pixel

Used when the only information required is

shape or outline info. For example:

To position a robotic gripper to grasp an object. To check a manufactured object for deformations.

For facsimile (FAX) images.


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Binary Images


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Binary Images

Binary images are often

created from gray-scale

images via a thresholdoperation. White (1) if pixel value

is larger than threshold.

Black (0) if it is less.


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Gray-Scale Images

Also referred to as monochrome or one-color

images.

Contain only brightness information. No color

information.

Typically contain 8 bits/pixel data, which

corresponds to 256 (0 to 255) differentbrightness (gray) levels.


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Gray-Scale Images

Why 8 bits/pixel? Provides more than adequate brightness

resolution.

Provides a noise margin by allowing

approximately twice gray levels as required.

Byte (8-bits) is the standard small unit in

computers.


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Gray-Scale Images

However, there are applications such as

medical imaging or astronomy that requires

12 or 16 bits/pixel. Useful when a small section of the image is

enlarged.

Allows the user to repeatedly zoom a specific area

in the image.


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Color Images

Modeled as three band monochrome image

data.

The values correspond to the brightness in

each spectral band.

Typical color images are represented as red,

green and blue (RGB) images.


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Color Images

Using the 8-bit standard model, a color image

would have 24 bits/pixel. 8-bits for each of the three color bands (red, green

and blue).


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Color Images

One example is the hue/saturation/lightness

(HSL) color transform. Hue: Color (green, blue, orange, etc).

Saturation: How much white is in the color (pink is

red with more white, so it is less saturated than

pure red).

Lightness: The brightness of the color.


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Color Images

Most people can relate to this method of

describing color. A deep, bright orange would have a large

intensity (bright), a hue of orange and a high value

of saturation (deep).

It is easier to picture this color in mind.

If we define this color in terms of RGB component,

R = 245, G = 110, B = 20, we have no idea how

this color looks like.


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Color Images

In addition to HSL, there are various other

formats used for representing color images: YCrCb

SCT (Spherical Coordinate Transform)

PCT (Principle Component Transform)

CIE XYZ L*u*v

L*a*b


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Color Images

One color space can be converted to another

color space by using equations.

Example: Converting RGB color space to

YCrCb color space.


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Multispectral Images

Typically contain information outside normal

human perceptual range. Infrared, ultraviolet, X-ray, acoustic or radar data.

They are not really images in usual sense

(not representing scene of physical world, but

rather information such as depth). Values are represented in visual form by

mapping the different spectral bands to RGB.


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Sources include satellite system, underwater

sonar system, airborne radar, infrared

imaging systems, and medical diagnostic

imaging systems.

The number of bands into which the data are

divided depends on the sensitivity of theimaging sensory.


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Most satellite images contain two to seven

spectral bands. One to three in the visible spectrum.

One or more in the infrared region.

Newest satellites have sensors that collect

image information in 30 or more bands.Due to the large amount of data involved,

compression is essential.


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Digital Image File Formats

There are many different types of image file

formats. This is because: There are many different types of images and

applications with varying requirements.

Lack of coordination within imaging industry.

Images can be converted from one format toanother using image conversion software.


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Types of image data are divided into two

categories: Bitmap (raster) images: where we have pixel data

and the corresponding brightness values stored in

some file format.

Vector images: methods of representing lines,

curves and shapes by storing only the key points.The process of turning the key points into an image

is called rendering.


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Most of the file formats to be discussed fall

under the category of bitmap images.

Some of the formats are compressed. The I(r, c) values are not available until the file is

decompressed.

Bitmap image files must contain both headerinformation and the raw pixel data.


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The header contain information regarding: The number of rows (height)

The number of columns (width)

The number of bands

The number of bits per pixel

The file type Type of compression used (if applicable)


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BIN format: Only contain the raw data I(r, c) and no header.

Users must know the necessary parameters

beforehand.

PPM format:

Contain raw image data with a simple header. PBM (binary), PGM (gray-scale), PPM (color) and

PNM (handles any of the other types).


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GIF (Graphics Interchange Format): Commonly used in WWW.

Limited to a maximum of 8 bits/pixel (256 colors).

The bits are used as an input to a lookup table.

Allow for a type of compression called LZW.

Image header is 13 bytes long.


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TIFF (Tagged Image File Format): Allows a maximum of 24 bits/pixel.

Support several types of compression: RLE, LZW,

and JPEG.

Header is of variable size and is arranged in a

hierarchical manner.

Designed to allow user to customize it for specific

applications.


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JFIF (JPEG File Interchange Format): Allows images compressed with JPEG algorithm to

be used in many different computer platforms.

Contains a Start of Image (SOI) and an application

(APPO) marker that serves as a file header.

Being used extensively in WWW.


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Sun Raster file format: Defined to allow for any number of bits per pixel.

Supports RLE compression and color lookup

tables.

Contains 32-byte header, followed by the image

data.


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SGI file format: Handles up to 16 million colors.

Supports RLE compression.

Contains 512-byte header, followed the image

data.

Majority of the bytes in header are not used,

presumably for future extension.


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EPS (Encapsulated PostScript): Not a bitmap image. The file contains text.

It is a language that supports more than justimages. Commonly used in desktop publishing.

Directly supported by many printers (in thehardware itself).

Commonly used for data interchange acrosshardware and software platforms.

The files are very big.

introduction to image file formats

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