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Digital Image Processing:
Introduction

Introduction
“One picture is worth more than ten thousand
words”
Anonymous

Contents
This lecture will cover:
◦ What is a digital image?
◦ What is digital image processing?
◦ History of digital image processing
◦ State of the art examples of digital image
processing
◦ Key stages in digital image processing

What is a Digital Image?
A digital image is a representation of a two-
dimensional image as a finite set of digital
values, called picture elements or pixels

Pixel values typically represent gray levels,
colours, heights, opacities etc
Remember digitization implies that a digital
image is an approximation of a real scene
1 pixel

Common image formats include:
◦ 1 sample per point (B&W or Grayscale)
◦ 3 samples per point (Red, Green, and Blue)
◦ 4 samples per point (Red, Green, Blue, and “Alpha”,
a.k.a. Opacity)

128 230 123
232 123 321
123 77 89
80 255 255
The figure is an example of digital
image that you are now viewing
on your computer screen. But
actually , this image is nothing but
a two dimensional array of
numbers ranging between 0 and
255.
Each number represents the value
of the function f(x,y) at any point.
In this case the value 128 , 230
,123 each represents an individual
pixel value. The dimensions of the
picture is actually the dimensions
of this two dimensional array.

What is Digital Image Processing?
Digital image processing focuses on two
major tasks
◦ Improvement of pictorial information for
human interpretation
◦ Processing of image data for storage,
transmission and representation for
autonomous machine perception.

How it works
In the above figure , an image has been captured by a camera and has been
sent to a digital system to remove all the other details , and just focus on
the water drop by zooming it in such a way that the quality of the image
remains the same.

Introduction
 Signal processing is a discipline in electrical engineering
and in mathematics that deals with analysis and processing
of analog and digital signals , and deals with storing ,
filtering , and other operations on signals.
 These signals include transmission signals , sound or voice
signals , image signals , and other signals e.t.c.
 Out of all these signals , the field that deals with the type
of signals for which the input is an image and the output is
also an image is done in image processing. As it name
suggests, it deals with the processing on images.
 It can be further divided into analog image processing and
digital image processing.

 Analog image processing
Analog image processing is done on analog signals. It
includes processing on two dimensional analog signals. In
this type of processing, the images are manipulated by
electrical means by varying the electrical signal. The
common example include is the television image.
 Digital image processing
The digital image processing deals with developing a digital
system that performs operations on an digital image.
Digital image processing has dominated over analog image
processing with the passage of time due its wider range of
applications.

The continuum from image processing to
computer vision can be broken up into low-,
mid- and high-level processes
Low Level Process
Input: Image
Output: Image
Examples: Noise
removal, image
sharpening
Mid Level Process
Input: Image
Output: Attributes
Examples: Object
recognition, segmentation
High Level Process
Input: Attributes
Output: Understanding
Examples: Scene
understanding,
autonomous navigation
In this course we will stop here

History of Digital Image Processing
Early 1920s: One of the first applications of
digital imaging was in the news-
paper industry
◦ The Bartlane cable picture
transmission service
◦ Images were transferred by submarine cable
between London and New York
◦ Pictures were coded for cable transfer and
reconstructed at the receiving end on a telegraph
printer
Early digital image

History of DIP (cont…)
Mid to late 1920s: Improvements to the
Bartlane system resulted in higher quality
images
◦ New reproduction
processes based
on photographic
techniques
◦ Increased number
of tones in
reproduced images Improved
digital image Early 15 tone digital image

1960s: Improvements in computing
technology and the onset of the space race led
to a surge of work in digital image processing
◦ 1964: Computers used to
improve the quality of
images of the moon taken
by the Ranger 7 probe
◦ Such techniques were used
in other space missions
including the Apollo landings A picture of the moon taken by
the Ranger 7 probe minutes
before landing

1970s: Digital image processing begins to be
used in medical applications
◦ 1979: Sir Godfrey N.
Hounsfield & Prof. Allan M.
Cormack share the Nobel
Prize in medicine for the
invention of tomography,
the technology behind
Computerised Axial
Tomography (CAT) scans
Typical head slice CAT image

1980s - Today: The use of digital image
processing techniques has exploded and they
are now used for all kinds of tasks in all
kinds of areas
◦ Image enhancement/restoration
◦ Artistic effects
◦ Medical visualisation
◦ Industrial inspection
◦ Law enforcement
◦ Human computer interfaces

Examples: Image Enhancement
One of the most common uses of DIP
techniques: improve quality, remove noise
etc

Examples: The Hubble Telescope
Launched in 1990 the Hubble
telescope can take images of
very distant objects
However, an incorrect mirror
made many of Hubble’s
images useless
Image processing
techniques were
used to fix this

Examples: Artistic Effects
Artistic effects are
used to make images
more visually
appealing, to add
special effects and to
make composite
images

Examples: Medicine
Take slice from MRI scan of canine heart, and find
boundaries between types of tissue
◦ Image with gray levels representing tissue density
◦ Use a suitable filter to highlight edges
Original MRI Image of a Dog Heart Edge Detection Image

Examples: GIS
Geographic Information Systems
◦ Digital image processing techniques are used
extensively to manipulate satellite imagery
◦ Terrain classification
◦ Meteorology

Examples: GIS (cont…)
Night-Time Lights of
the World data set
◦ Global inventory of
human settlement
◦ Not hard to imagine
the kind of analysis
that might be done
using this data

Examples: Industrial Inspection
Human operators are
expensive, slow and
unreliable
Make machines do the
job instead
Industrial vision systems
are used in all kinds of
industries
Can we trust them?

Examples: PCB Inspection
Printed Circuit Board (PCB) inspection
◦ Machine inspection is used to determine that
all components are present and that all solder
joints are acceptable
◦ Both conventional imaging and x-ray imaging
are used

Examples: Law Enforcement
Image processing
techniques are used
extensively by law
enforcers
◦ Number plate
recognition for speed
cameras/automated toll
systems
◦ Fingerprint recognition
◦ Enhancement of CCTV
images

Examples: HCI
Try to make human
computer interfaces more
natural
◦ Face recognition
◦ Gesture recognition

Applications of Digital Image
Processing
 Image sharpening and restoration
 Medical field
 Remote sensing
 Transmission and encoding
 Machine/Robot vision
 Color processing
 Pattern recognition
 Video processing
 Microscopic Imaging
 Others

Image sharpening and restoration
 Image sharpening and restoration refers here to process images
that have been captured from the modern camera to make them
a better image or to manipulate those images in way to achieve
desired result. It refers to do what Photoshop usually does.
 This includes Zooming, blurring , sharpening , gray scale to
color conversion, detecting edges and vice versa , Image
retrieval and Image recognition. The common examples are:
Original Zoomed Blurr

UV imaging
 In the field of remote sensing , the area of the earth is scanned by a
satellite or from a very high ground and then it is analyzed to obtain
information about it. One particular application of digital image
processing in the field of remote sensing is to detect infrastructure
damages caused by an earthquake.

Hurdle detection
 Hurdle detection is one of the common task that has been
done through image processing, by identifying different type
of objects in the image and then calculating the distance
between robot and hurdles.

Line follower robot
 Most of the robots today work by following the line and thus are
called line follower robots. This help a robot to move on its path
and perform some tasks. This has also been achieved through image
processing.

Fundamental Steps in Digital Image Processing:
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement Representation
& Description
Problem Domain
Colour Image
Processing
Image
Compression
Wavelets &
Multiresolution
processing
Outputs of these processes generally are images
Knowledge Base

Step 1: Image Acquisition
The image is captured by a sensor (eg. Camera),
and digitized if the output of the camera or
sensor is not already in digital form, using
analogue-to-digital convertor

Step 2: Image Enhancement
The process of manipulating an image so that the result
is more suitable than the original for specific
applications.
The idea behind enhancement techniques is to bring out
details that are hidden, or simple to highlight certain
features of interest in an image.

Step 3: Image Restoration
- Improving the appearance of an image
- Tend to be mathematical or probabilistic models.
Enhancement, on the other hand, is based on human
subjective preferences regarding what constitutes a
“good” enhancement result.

Step 4: Colour Image Processing
Use the colour of the image to extract features of interest
in an image.
Colour modeling and processing in a digital domain etc.

Step 5: Wavelets
Are the foundation of representing images in various
degrees of resolution. It is used for image data
compression where images are subdivided into smaller
regions.

Step 6: Compression
Techniques for reducing the storage required to
save an image or the bandwidth required to
transmit it.

Tools for extracting image
components that are useful in
the representation and
description of shape.
In this step, there would be a
transition from processes that
output images, to processes that
output image attributes.
Step 7: Morphological Processing

Step 8: Image Segmentation
Segmentation procedures partition an image into its
constituent parts or objects.
Important Tip: The more accurate the segmentation,
the more likely recognition is to succeed.

Step 9: Representation and Description
- Representation: Make a decision whether the data should
be represented as a boundary or as a complete region. It is
almost always follows the output of a segmentation stage.
- Boundary Representation: Focus on external shape
characteristics, such as corners and inflections.
- Region Representation: Focus on internal properties,
such as texture or skeleton shape.
Transforming raw data into a form suitable for subsequent
computer processing. Description deals with extracting
attributes that result in some quantitative information of
interest or are basic for differentiating one class of objects
from another.

Step 10: Object Recognition
Recognition: the process that assigns label to an object
based on the information provided by its description.
Recognition is the process that assigns a label, such as,
“vehicle” to an object based on its descriptors.

Components of an Image Processing
System
Network
Image displays Computer Mass storage
Hardcopy
Specialized image
processing hardware
Image processing
software
Image sensors
Problem Domain
Typical general-
purpose DIP
system

System
1. Image Sensors
Two elements are required to acquire
digital images. The first is the physical
device that is sensitive to the energy
radiated by the object we wish to image
(Sensor). The second, called a digitizer,
is a device for converting the output of
the physical sensing device into digital
form.

System
2. Specialized Image Processing Hardware
Usually consists of the digitizer, mentioned before, plus
hardware that performs other primitive operations, such as an
arithmetic logic unit (ALU), which performs arithmetic and
logical operations in parallel on entire images.
This type of hardware sometimes is called a front-end
subsystem, and its most distinguishing characteristic is speed.
In other words, this unit performs functions that require fast
data throughputs that the typical main computer cannot handle.

System
4. Image Processing Software
Software for image processing consists of specialized
modules that perform specific tasks. A well-designed
package also includes the capability for the user to write
code that, as a minimum, utilizes the specialized modules.

System
5. Mass Storage Capability
Mass storage capability is a must in a image processing
applications. And image of sized 1024 * 1024 pixels
requires one megabyte of storage space if the image is not
compressed.
Digital storage for image processing applications falls
into three principal categories:
1. Short-term storage for use during processing.
2. on line storage for relatively fast recall
3. Archival storage, characterized by infrequent access

System
5. Mass Storage Capability
One method of providing short-term storage is computer memory.
Another is by specialized boards, called frame buffers, that store
one or more images and can be accessed rapidly.
The on-line storage method, allows virtually instantaneous image
zoom, as well as scroll (vertical shifts) and pan (horizontal shifts).
On-line storage generally takes the form of magnetic disks and
optical-media storage. The key factor characterizing on-line
storage is frequent access to the stored data.
Finally, archival storage is characterized by massive storage
requirements but infrequent need for access.

System
6. Image Displays
The displays in use today are mainly color (preferably
flat screen) TV monitors. Monitors are driven by the
outputs of the image and graphics display cards that are
an integral part of a computer system.

System
7. Hardcopy devices
Used for recording images, include laser
printers, film cameras, heat-sensitive
devices, inkjet units and digital units,
such as optical and CD-Rom disks.

System
8. Networking
Is almost a default function in any computer
system, in use today. Because of the large
amount of data inherent in image processing
applications the key consideration in image
transmission is bandwidth.
In dedicated networks, this typically is not a
problem, but communications with remote sites
via the internet are not always as efficient.

Summary
We have looked at:
◦ What is a digital image?
◦ What is digital image processing?
◦ History of digital image processing
◦ State of the art examples of digital image
processing
◦ Key stages in digital image processing
Next time we will start to see how it all
works…

Digital image = a multidimensional
array of numbers (such as intensity image)
or vectors (such as color image)
Each component in the image
called pixel associates with the
pixel value (a single number in
the case of intensity images or a
vector in the case of color
images).
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Visual Perception: Human Eye
(Picture from Microsoft Encarta 2000)

Cross Section of the Human Eye
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.

1. The lens contains 60-70% water, 6% of fat.
2. The iris diaphragm controls amount of light that enters the eye.
3. Light receptors in the retina
- About 6-7 millions cones for bright light vision called photopic
- Density of cones is about 150,000 elements/mm2.
- Cones involve in color vision.
- Cones are concentrated in fovea about 1.5x1.5 mm2.
- About 75-150 millions rods for dim light vision called scotopic
- Rods are sensitive to low level of light and are not involved
color vision.
4. Blind spot is the region of emergence of the optic nerve from the eye.
Visual Perception: Human Eye (cont.)

Blind-Spot Experiment
Draw an image similar to that below on a
piece of paper (the dot and cross are about 6
inches apart)
Close your right eye and focus on the cross
with your left eye
Hold the image about 20 inches away from
your face and move it slowly towards you
The dot should disappear!

Image Formation In The Eye
Muscles within the eye can be used to
change the shape of the lens allowing us
focus on objects that are near or far away
An image is focused onto the retina causing
rods and cones to become excited which
ultimately send signals to the brain

Position
Intensity
Brightness Adaptation of Human Eye : Mach Band Effect

Mach Band Effect
Intensities of surrounding points effect
perceived brightness at each point.
In this image, edges between bars
appear brighter on the right side and
darker on the left side.

In area A, brightness perceived is darker while in area B is
brighter. This phenomenon is called Mach Band Effect.
Position
Intensity A
B
Mach Band Effect (Cont)

Simultaneous contrast. All small squares have exactly the same intensity
but they appear progressively darker as background becomes lighter.
Brightness Adaptation of Human Eye : Simultaneous Contrast

Simultaneous Contrast

Optical illusion

Visible Spectrum

Image Sensors
Single sensor
Line sensor
Array sensor

Image Sensors : Single Sensor

Image Sensors : Line Sensor
Fingerprint sweep sensor
Computerized Axial Tomography

Image “After snow storm”
Fundamentals of Digital Images
f(x,y)
x
y
w An image: a multidimensional function of spatial coordinates.
w Spatial coordinate: (x,y) for 2D case such as photograph,
(x,y,z) for 3D case such as CT scan images
(x,y,t) for movies
w The function f may represent intensity (for monochrome images)
or color (for color images) or other associated values.
Origin

Conventional Coordinate for Image Representation

Digital Image Types : Intensity Image
Intensity image or monochrome image
each pixel corresponds to light intensity
normally represented in gray scale (gray
level).
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Gray scale values

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Digital Image Types : RGB Image
Color image or RGB image:
each pixel contains a vector
representing red, green and
blue components.
RGB components

Image Types : Binary Image
Binary image or black and white image
Each pixel contains one bit :
1 represent white
0 represents black
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0
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0
0
Binary data

Image Types : Index Image
Index image
Each pixel contains index number
pointing to a color in a color table
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Index value
Index
No.
Red
component
Green
component
Blue
component
1 0.1 0.5 0.3
2 1.0 0.0 0.0
3 0.0 1.0 0.0
4 0.5 0.5 0.5
5 0.2 0.8 0.9
… … … …
Color Table

Digital Image Acquisition Process

Basic Relationship of Pixels
x
y
(0,0)
Conventional indexing method
(x,y) (x+1,y)
(x-1,y)
(x,y-1)
(x,y+1)
(x+1,y-1)
(x-1,y-1)
(x-1,y+1) (x+1,y+1)

Neighbors of a Pixel
p (x+1,y)
(x-1,y)
(x,y-1)
(x,y+1)
4-neighbors of p:
N4(p) =
(x-1,y)
(x+1,y)
(x,y-1)
(x,y+1)
Neighborhood relation is used to tell adjacent pixels. It is useful for analyzing
regions.
Note: q N4(p) implies p N4(q)
4-neighborhood relation considers only vertical and horizontal neighbors.

p (x+1,y)
(x-1,y)
(x,y-1)
(x,y+1)
(x+1,y-1)
(x-1,y-1)
(x-1,y+1) (x+1,y+1)
Neighbors of a Pixel (cont.)
8-neighbors of p:
(x-1,y-1)
(x,y-1)
(x+1,y-1)
(x-1,y)
(x+1,y)
(x-1,y+1)
(x,y+1)
(x+1,y+1)
N8(p) =
8-neighborhood relation considers all neighbor pixels.

p
(x+1,y-1)
(x-1,y-1)
(x-1,y+1) (x+1,y+1)
Diagonal neighbors of p:
ND(p)=
(x-1,y-1)
(x+1,y-1)
(x-1,y+1)
(x+1,y+1)
Neighbors of a Pixel (cont.)
Diagonal -neighborhood relation considers only diagonal
neighbor pixels.

Connectivity
Connectivity is adapted from neighborhood relation.
Two pixels are connected if they are in the same class (i.e. the same
color or the same range of intensity) and they are neighbors of one
another.
For p and q from the same class
w 4-connectivity: p and q are 4-connected if q Î N4(p)
w 8-connectivity: p and q are 8-connected if q Î N8(p)
w mixed-connectivity (m-connectivity):
p and q are m-connected if q Î N4(p) or
q Î ND(p) and N4(p) Ç N4(q) = Æ

Adjacency
A pixel p is adjacent to pixel q is they are connected.
Two image subsets S1 and S2 are adjacent if some pixel in S1 is adjacent
to some pixel in S2
S1
S2
We can define type of adjacency: 4-adjacency, 8-adjacency or m-
adjacency depending on type of connectivity.

Types of Adjacency
1. 4-adjacency: Two pixels p and q with
values from V are 4-adjacent if q is in the
set N4(p).
2. 8-adjacency: Two pixels p and q with
values from V are 8-adjacent if q is in the
set N8(p).
3. m-adjacency =(mixed)

Types of Adjacency
 m-adjacency:
Two pixels p and q with values from V
are m-adjacent if :
 q is in N4(p) or
 q is in ND(p) and the set N4(p) ∩ N4(q) has no
pixel whose values are from V (no intersection)
 Important Note: the type of adjacency
used must be specified

Types of Adjacency
 Mixed adjacency is a modification of 8-
adjacency. It is introduced to eliminate the
ambiguities that often arise when 8-adjacency
is used.
 For example:

Types of Adjacency
 In this example, we can note that to connect between
two pixels (finding a path between two pixels):
◦ In 8-adjacency way, you can find multiple paths
between two pixels
◦ While, in m-adjacency, you can find only one path
between two pixels
 So, m-adjacency has eliminated the multiple path
connection that has been generated by the 8-adjacency.
 Two subsets S1 and S2 are adjacent, if some pixel in S1
is adjacent to some pixel in S2. Adjacent means, either
4-, 8- or m-adjacency.

Path
A path from pixel p at (x,y) to pixel q at (s,t) is a sequence of distinct
pixels:
(x0,y0), (x1,y1), (x2,y2),…, (xn,yn)
such that
(x0,y0) = (x,y) and (xn,yn) = (s,t)
and
(xi,yi) is adjacent to (xi-1,yi-1), i = 1,…,n
p
q
We can define type of path: 4-path, 8-path or m-path depending on type of
adjacency.

Path (cont.)
p
q
p
q
p
q
8-path from p to q
results in some ambiguity
m-path from p to q
solves this ambiguity
8-path m-path

Distance
 For pixels p, q and z, with coordinates (x,y), (s,t) and (v,w),
respectively, D is a distance function if:
(a) D (p,q) ≥ 0 (D (p,q) = 0 iff p = q),
(b) D (p,q) = D (q, p), and
(c) D (p,z) ≤ D (p,q) + D (q,z).

Distance (cont.)
D4-distance (city-block distance) is defined as
t
y
s
x
q
p
D -
+
-

)
,
(
4
1 2
1
0
1 2
1
2
2
2
2
2
2
Pixels with D4(p) = 1 is 4-neighbors of p.

Distance (cont.)
D8-distance (chessboard distance) is defined as
)
,
max(
)
,
(
8 t
y
s
x
q
p
D -
-

1
2
1
0
1
2
1
2
2
2
2
2
2
Pixels with D8(p) = 1 is 8-neighbors of p.
2
2
2
2
2
2
2
2
1
1
1
1

Distance Measures
 The Euclidean Distance between p and q
is defined as:
De (p,q) = [(x – s)2 + (y - t)2]1/2
Pixels having a distance less than or equal
to some value r from (x,y) are the points
contained in a disk of
radius r centered at (x,y)
p (x,y)
q (s,t)

Distance Measures
 The D4 distance (also called city-block distance)
between p and q is defined as:
D4 (p,q) = | x – s | + | y – t |
Pixels having a D4 distance from
(x,y), less than or equal to some
value r form a Diamond
centered at (x,y)
p (x,y)
q (s,t)
D4

Distance Measures
Example:
The pixels with distance D4 ≤ 2 from (x,y) form the
following contours of constant distance.
The pixels with D4 = 1 are
the 4-neighbors of (x,y)

Distance Measures
 The D8 distance (also called chessboard distance)
between p and q is defined as:
D8 (p,q) = max(| x – s |,| y – t |)
Pixels having a D8 distance from
(x,y), less than or equal to some
value r form a square
Centered at (x,y)
p (x,y)
q (s,t)
D8(b)
D8(a)
D8 = max(D8(a) , D8(b))

Distance Measures
Example:
D8 distance ≤ 2 from (x,y) form the
following contours of constant distance.

Distance Measures
 Dm distance:
is defined as the shortest m-path between the
points.
In this case, the distance between two pixels
will depend on the values of the pixels along
the path, as well as the values of their
neighbors.

Distance Measures
 Example:
Consider the following arrangement of pixels
and assume that p, p2, and p4 have value 1 and
that p1 and p3 can have can have a value of 0 or
1
Suppose that we consider
the adjacency of pixels
values 1 (i.e. V = {1})

Distance Measures
 Cont. Example:
Now, to compute the Dm between points p
and p4
Here we have 4 cases:
Case1: If p1 =0 and p3 = 0
The length of the shortest m-path
(the Dm distance) is 2 (p, p2, p4)

Distance Measures
 Cont. Example:
now, p1 and p will no longer be adjacent
(see m-adjacency definition)
then, the length of the shortest
path will be 3 (p, p1, p2, p4)

Distance Measures
 Cont. Example:
The same applies here, and the shortest –
m-path will be 3 (p, p2, p3, p4)

Distance Measures
 Cont. Example:
The length of the shortest m-path will be
4 (p, p1 , p2, p3, p4)

Image Sampling and Quantization
Image sampling: discretize an image in the spatial domain
Spatial resolution / image resolution: pixel size or number of pixels

How to choose the spatial resolution
= Sampling locations
Original
image
Sampled
image
Under sampling, we lost some image details!
Spatial resolution

How to choose the spatial resolution : Nyquist Rate
Original
image
= Sampling locations
Minimum
Period
Spatial resolution
(sampling rate)
Sampled image
No detail is lost!
Nyquist Rate:
Spatial resolution must be less or
equal half of the minimum period of
the image or sampling frequency
must be greater or Equal twice of the
maximum frequency.
2mm
1mm

0 0.5 1 1.5 2
-1
-0.5
0
0.5
1
0 0.5 1 1.5 2
-1
-0.5
0
0.5
1
1
),
2
sin(
)
(
1 
 f
t
t
x 
6
),
12
sin(
)
(
2 
 f
t
t
x 
Sampling rate:
5 samples/sec
Aliased Frequency
Two different frequencies but the same results !

Effect of Spatial Resolution
256x256 pixels
64x64 pixels
128x128 pixels
32x32 pixels

Effect of Spatial Resolution

Moire Pattern Effect : Special Case of Sampling
Moire patterns occur when frequencies of two superimposed
periodic patterns are close to each other.

Can we increase spatial resolution by interpolation ?
Down sampling is an irreversible process.

Image Quantization
Image quantization:
discretize continuous pixel values into discrete numbers
Color resolution/ color depth/ levels:
- No. of colors or gray levels or
- No. of bits representing each pixel value
- No. of colors or gray levels Nc is given by
b
c
N 2

where b = no. of bits

Quantization function
Light intensity
Quantization
level
0
1
2
Nc-1
Nc-2
Darkest Brightest

Effect of Quantization Levels
256 levels 128 levels
32 levels
64 levels

Effect of Quantization Levels (cont.)
16 levels 8 levels
2 levels
4 levels
In this image,
it is easy to see
false contour.

How to select the suitable size and pixel depth of images
Low detail image Medium detail image High detail image
Lena image Cameraman image
To satisfy human mind
1. For images of the same size, the low detail image may need more pixel depth.
2. As an image size increase, fewer gray levels may be needed.
The word “suitable” is subjective: depending on “subject”.

Human vision: Spatial Frequency vs Contrast

Human vision: Distinguish ability for Difference in brightness
Regions with 5% brightness difference

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