Level-1 (Mandatory)
As
Per RGPV Syllabus
Que.
1 Illustrate the reason behind refreshing the screen many times in a second?
Ans:-
The refresh
rate (most commonly the "vertical refresh rate",
"vertical scan rate" for cathode ray
tubes) is the number of times in a second that a display hardware
updates its buffer.
Reason On
cathode ray tube (CRT) displays, increasing the refresh rate decreases flickering, thereby reducing eye strain.
However, if a refresh rate is specified that is beyond what is recommended for
the display, damage to the display can occur.
Que.
2 Define frame buffer and provide the memory requirement for a black and white
and a colorful image as well.
Ans:
FRAME BUFFER
A frame buffer, or
picture memory, is a computer memory
organized into an m x
n rectangular grid of dots, called picture
elements, or pixels
for short. Each pixel requires a certain
number of bits,
varying from 1 in bit-map displays to 24 or more
in high-quality color
displays. We call the number of bits per
pixel the
"depth" of the frame buffer.
A common size of
frame buffers is m=n=512, at a depth of 8 bits:
512x512x8. A picture this size takes up 256 Kilobytes.
BLACK AND WHITE
Some early grayscale
monitors can only show up to sixteen (4-bit) different shades, but
today grayscale images (as photographs) intended for visual display (both on
screen and printed) are commonly stored with 8 bits per sampled pixel, which
allows 256 different intensities
·
Black and White Display: 512 x 512 x 1bit
memory is required
·
24 bit color display: 512 x 512 x 24bit
(each pixel has 8 bits for red, 8 bits for green and 8 bits for blue.)
·
8 bit color display: Want benefits of 24
bit color with only 8 bit display? 512 x 512 x 8bit (each pixel is 8 bits deep,
so values 0-255 are possible).
|
Image
Type
|
|
Bytes
per pixel
|
|
1 bit
Line art
|
|
1/8
byte per pixel
(1 bit per pixel, 8 bits per byte) |
|
8 bit
Grayscale
|
|
1
byte per pixel
|
|
16
bit Grayscale
|
|
2
bytes per pixel
|
|
24
bit RGB
|
|
3
bytes per pixel
Most common for photos, for example JPG |
|
32
bit CMYK
|
|
4
bytes per pixel, for Prepress
|
|
48
bit RGB
|
|
6
bytes per pixel
|
Que.
3 State the purpose of scan conversion, along with the side effects.
Ans :
Purpose : A fundamental operation that is used
extensively in computer graphics and visualization is the process of scan conversion
or rasterization. Given a polygon in image space, this process determines the
pixels that intersect the polygon. This process is utilized in visible-surface
algorithms, incremental-shading techniques, polygon-fill algorithms,
ray-tracing-acceleration algorithms, and a number of other tasks that are
critical to the understanding of the computer graphics field.
Aliasing Effects (Side effects of scan conversion) Scan
conversion is essentially a systematic approach to mapping objects that are
defined in continuous space to their discrete approximation. The various forms
of distortion that result from this operation are collectively referred to as
the aliasing effects of scan conversion.
1. Staircase A common example of
aliasing effects is the staircase of jagged appearance, we see when scan
converting a primitive such as a line of a circle. We also see the stair steps
of jaggiest along the border of a filled region.
2. Unequal Brightness Another side
effect that is less noticeable is the unequal brightness of lines of different
orientation. A slanted line appears dimmer than a horizontal of vertical line
although all are presented at the same intensity level. The reason for this
problem can be explained using Fig. below where the pixels on the horizontal
line are placed one unit apart, whereas those on the diagonal line are
approximately 1.414 units apart. This difference in density produces the
perceived difference in brightness.
Beyond Syllabus
Que.
1 Raster scan system
are more economical than random scan system. Explain the statement.
Ans:
Raster Scan
methods have increasingly become the dominant technology since about 1975.
These methods use the TV type raster scan. The growth in the use of such
methods has been dependent on rapidly decreasing memory
prices and on the availability of cheap scan generating hardware from the TV
industry.
The screen is coated with discrete dots of phosphor, usually
called pixels, laid out in a rectangular array. The image is then determined by
how each pixel is intensified. The representation of the image used in
servicing the refresh system is thus an area of memory holding a value for each
pixel. This memory area holding the image representation is called the frame
buffer.
The values in the frame buffer are held as a sequence of
horizontal lines of pixel values from the top of the screen down. The scan
generator then moves the beam in a series of horizontal lines with fly-back
(non-intensified) between each line and between the end of the frame and the
beginning of the next frame.
Unlike random-scan which is a line drawing device, refresh
CRT is a point-plotting device. Raster displays store the display primitives
(lines, characters, shaded and patterned areas) in a refresh buffer. Refresh
buffer (also called frame buffer) stores the drawing primitives in terms of
points and pixels components This scan is synchronized with the access of the
intensity values held in the frame buffer.
Random Scan Display
Random scan displays, often termed vector Vector, Stroke, and
Line drawing displays, came first and are still used in some applications. Here
the characters are also made of sequences of strokes (or short lines). The
electron gun of a CRT illuminates straight lines in any order. The display
processor repeatedly reads a variable 'display file' defining a sequence of X,Y
coordinate pairs and brightness or color values, and converts these to voltages
controlling the electron gun.
In random scan display an electron beam is deflected from
endpoint to end-point.
The order of deflection is dictated by the arbitrary
order of the display commands. The display must be refreshed at regular
intervals – minimum of 30 Hz (fps) for flicker-free display
Que.
2 Throw light on the
following terms-
Ans:
(a) Interlacing
Interlacing
(also known as interleaving) is a method of encoding a bitmap
image such that a person who has partially received it sees a degraded copy of
the entire image. When communicating over a slow communications link, this is
often preferable to seeing a perfectly clear copy of one part of the image, as
it helps the viewer decide more quickly whether to abort or continue the transmission.
Interlacing is a form of incremental
decoding, because the image can be loaded incrementally. Another form of
incremental decoding is progressive scan. In progressive scan the
loaded image is decoded line for line, so instead of becoming incrementally
clearer it becomes incrementally larger. The main difference between the
interlace concept in bitmaps and in video is that even progressive bitmaps can
be loaded over multiple frames.For example: Interlaced GIF is a GIF image that seems to arrive on your display like an image coming through a slowly opening Venetian blind. A fuzzy outline of an image is gradually replaced by seven successive waves of bit streams that fill in the missing lines until the image arrives at its full resolution.
(b) Persistence
of phosphorus
A phosphor,
most generally, is a substance that exhibits the phenomenon of luminescence.
Somewhat confusingly, this includes both phosphorescent
materials, which show a slow decay in brightness (> 1 ms), and fluorescent
materials, where the emission decay takes place over tens of nanoseconds.
The time it takes the emitted light from the screen to
decay one tenth of its original intensity is called as persistence.
(c) Deflection
system inside a CRT
ELECTROMAGNETIC DEFLECTION. Electromagnetic
deflection uses a magnetic field generated
by four coils to move the beam across
the CRT. Electromagnetic deflection is
commonly found on CRTs that use a
raster-scan type display. Current flows through the
electron beam as it moves from the
electron gun (cathode) to
the phosphor face (anode) of the CRT.
This current develops a circular magnetic field. By introducing an
external magnetic field, the
beam can be deflected.
Controlling the polarity and strength of
this external field controls the amount and
direction of the beam deflection. The magnetic
field is introduced into the CRT by
the yoke assembly. The yoke consists of four coils of wire
mounted at 90-degree increments.
The yoke is mounted around the neck of
the CRT.
Current
flowing through the coil produces a
magnetic field at a right angle to
the coil. The magnetic field will cause the electron beam to
deflect.
Que.
3 Compare the merits and demerits of CRT and Flat panel displays.
Ans:
Pros of LCD vs Cons of CRT
|
|
LCD |
CRT |
|
Slim Factor |
Slim |
Bulky |
|
Viewable screen |
Full or very close |
Usually 0.9 inches or less than actual size |
|
Screen Flatness |
True Flat |
Fake Flat (unless aperture grille) |
|
Radiation |
Little or none |
More Radiation |
|
Weight |
Light |
Heavy |
|
Power Requirements |
Low Power requirement |
250% or more power |
|
Glare |
No Glare |
Reduced Glare |
|
Image Sharpness |
Sharp |
Slightly less sharp images |
|
Automatic Resize |
Perfect |
Imperfect |
|
Burn-In |
None |
Suffers from burn-in problem |
|
Refresh Rate |
No refresh rate (60hz fixed) |
Needs refresh rate (minimum 72hz) |
|
Warmness |
Little |
Back gets warm after some time |
Pros of CRT vs Cons of LCD
|
|
CRT |
LCD |
|
Dead / Stuck Pixel |
No such problem |
May have dead / stuck pixel |
|
Response Rate |
No issue with response rate |
Slow |
|
Price |
Cheap |
Expensive |
|
Native Resolution |
None |
Has a native resolution |
|
Max Colors |
32 bit |
8-Bit max, 16.7 million colors. |
|
Viewing Angle |
Wide viewing angle |
Narrow viewing angle |
|
Video |
Ideal for any video viewing including HD |
Not ideal for videos, unless HD |
|
Blackness |
True Black |
Between Dark Gray to Gray |
Que. 4 Identify the relation between aspect ratio of the
raster and the image quality.
Comparison chart
Aspect Ratio |
Resolution |
|
|
Definition
|
Aspect ratio is the ratio of the width of an image to the
height of the image (x:y).
|
The resolution of an image is the total number of pixels
displayed on your computer or television screen.
|
|
About
|
Original aspect ratio (OAR) and Modified aspect ratio
(MAR) are the dimensions in which the film is originally produced, or altered
to fit a particular screen, respectively.
|
The resolution of digital images can be described as pixel
resolution, spatial resolution, spectral resolution, temporal and radiometric
resolution.
|
|
Commonly used
|
The common aspect ratios used are 1.33:1, 1.37:1, 1.43:1,
1.50:1, 1.56:1, 1.66:1, 1.75:1, 1.78:1, 1.85:1, 2.00:1, 2.20:1, 2.35:1,
2.39:1, 2.55:1 and other ratios.
|
The common monitor resolutions are 640x480, 800x600 and
1024x768.
|
Level-2 (Mandatory)
As Per RGPV Syllabus
Que.
1 Predict the time taken by an electron gun to refresh a monochrome image of
size 4 by 5 inch; with a resolution of 400 pixels per inch square. Given one
horizontal retrace takes 10 microseconds.
Ans:
SquareRoot(400) = 20
= no. of horizontal/vertical pixels per inch
No. of horizontal retraces = no.
pixel/inch * no. of vertical inch
= 20*5
= 100 retraces
Total Time = no. of retraces * time
for one ratrace
= 100*10 milliseconds
= 1,000 milliseconds
Que.
2 Find out the intermediate pixels of a line segment with endpoints at (10, 10)
and (20, 15). Use the DDA algorithm.
Ans:
|
S.No.
|
X
|
Y
|
|
1
|
10
|
10
|
|
2
|
11
|
10.5
|
|
3
|
12
|
11
|
|
4
|
13
|
11.5
|
|
5
|
14
|
12
|
|
6
|
15
|
12.5
|
|
7
|
16
|
13
|
|
8
|
17
|
13.5
|
|
9
|
18
|
14
|
|
10
|
19
|
14.5
|
|
11
|
20
|
15
|
Que.
3 Derive the decision perimeter of the bresenham’s algorithm for scan
conversion of a line with a slope greater than one.
Ans:
Beyond Syllabus
Que.
1 Discover the time complexity of the DDA and bresenham’s algorithms for
drawing a line.
Ans:
DDA Algorithm:
Plot the first end point of the line by using
putpixel(x0, y0,5) where 5 is the value of colour in
integer.
Find the values of
dx,dy.
If(dy>dx)
then
Steps=dy
Else
Steps=dx;
xi=dx/Steps;
yi=dy/Steps.
Repeat
the steps for k=1 to steps.
x=x+ xi;
y=y+ yi;
putpixel(x,y,5);
Time Complexity of
DDA algorithm O(max(|dx|,|dy|)).
Bresenham's Algorithms:
void Line( const float
x1, const float
y1, const float
x2, const float
y2, const Color& color )
{
// Bresenham's line algorithm
const
bool steep = (fabs(y2 - y1) > fabs(x2
- x1));
if(steep)
{
std::swap(x1, y1);
std::swap(x2, y2);
}
if(x1 > x2)
{
std::swap(x1, x2);
std::swap(y1, y2);
}
const
float dx = x2 - x1;
const
float dy = fabs(y2 - y1);
float
error = dx / 2.0f;
const
int ystep = (y1 < y2) ? 1 : -1;
int y = (int)y1;
const
int maxX = (int)x2;
for(int
x=(int)x1; x<maxX; x++)
{
if(steep)
{
SetPixel(y,x, color);
}
else
{
SetPixel(x,y, color);
}
error -= dy;
if(error < 0)
{
y += ystep;
error += dx;
}
}
}
Time complexity of Bresenham's algorithm O(max(|dx|,|dy|))
Que.
2 Interpret the reason behind sliding the circle to the origin before
implementing the mid point algorithm.
Ans: Our
objective is to simplify the function evaluation that takes place on each
iteration of our circle-drawing algorithm. All those multiply and square-root
evaluations are expensive. We can do better.
We translate
our coordinate system so that the circle's center is at the origin One approach
is to manipulate the circle equation slightly.
1.
First, we translate our coordinate
system so that the circle's center is at the origin (the book leaves out this
step), giving: ( ( x + x0 ) - x0 )2 + ( ( y - y 0 ) - y 0 )2 = r2 We simplify
and make the equation homogeneous
2.
Next, we simplify and make the
equation homogeneous (i.e. independent of a scaling of the independent
variables; making the whole equation equal to zero will accomplish this) by
subtracting r2 from both sides. x2 + y2 - r2 = 0 We can regard this expression as
a function in x and y.
Discriminating
function: partition the domain, into one of
three categories f( x, y ) = x2 + y2 - r2. Functions of this sort are called
discriminating functions in computer graphics. They have the property of
partitioning the domain, pixel coordinates in our case, into one of three
categories. When f(x,y) is equal to zero the point lies on the desired locus (a
circle in this case), when f(x, y) evaluates to a positive result the point
lies one side of the locus, and when f(x,y) evaluates to negative it lies on
the other side.
What we'd like
to do is to use this discriminating function to maintain our trajectory of
drawn pixels as close as possible to the desired circle. Luckily, we can start
with a point on the circle, (x 0 , y 0+r) (or ( 0, r) in our adjusted
coordinate system). As we move along in steps of x we note that the slope is
less than zero and greater than negative one at points in the direction we're
heading that are near our known point on a circle. Thus we need only to figure
out at each step whether to step down in y or maintain y at each step. We will
compute points between x=0 and x=y and then draw the 8 matching points In that
area, the slope of the curve is between 0 and -1 From each step/point (x, y),
the next one is either (x+1, y) or (x+1, y-1) We decide about which one by
looking at the midpoint M M inside (f<0) => next point is E M ouside
(f>0) => next point is SE Dold = f( x + 1, y - 1/2) = ( x + 1 )2 + ( y
-1/2 )2 - r2 = f(x,y) + 2x - y + 5/4 if Dold < 0, E is chosen (y) Dnew = f(x
+ 2, y -1/2) = ( x + 2 )2 + ( y -1/2 )2 - r2 = f(x,y) + 4x -y + 3 + 5/4 Dnew =
Dold + 2x + 3 = Dold + 2(x+1) +1 if Dold >0, ES is chosen (y-1) Dnew = f(x +
2, y - 3/2) = ( x + 2 )2 + ( y - 3/2 )2 - r2 = f(x,y) + 4x -3y + 5 + 5/4 Dnew =
Dold + 2x -2y + 5 = Dold + 2((x+1) -(y-1) + 1) D0 = f(1, r-1/2) = 5/4 - r
...not an inger Lets change every where D by P = D - 1/4 P0 = 1 - r , and
because increment are always integer, P < -1/4 <=> P < 0 int x = 0;
int y = radius; int p = 1 - radius; circlePoints(xCenter, yCenter, x, y, pix);
while (x < y) { x++; if (p < 0) { p += 2*x+1; } else { y--; p +=
2*(x-y+1); } circlePoints(xCenter, yCenter, x, y, pix); }
Level-3 (Attempt any 2)
Que.
1 Describe the term contrast; how does it enhance the picture quality?
Ans: Contrast is the difference in luminance or
colour that makes an object (or its representation in an image or display)
distinguishable. In visual perception of the real world, contrast is determined
by the difference in the color and brightness of the object and other objects
within the same field of view.
Relation of contrast with image quality Contrast, also known as gamma,
is the slope of the tone reproduction curve in a log-log space. High contrast
usually involves loss of dynamic range — loss of detail, or clipping, in
highlights or shadows.
Que.
2 List the side effets of scan conversion.
Ans: Aliasing Effects (Side effects of
scan conversion) Scan conversion is essentially a systematic
approach to mapping objects that are defined in continuous space to their discrete
approximation. The various forms of distortion that result from this operation
are collectively referred to as the aliasing effects of scan conversion.
1. Staircase A common example of aliasing effects
is the staircase of jagged appearance, we see when scan converting a primitive
such as a line of a circle. We also see the stair steps of jaggiest along the
border of a filled region.
2. Unequal
Brightness Another
side effect that is less noticeable is the unequal brightness of lines of
different orientation. A slanted line appears dimmer than a horizontal of
vertical line although all are presented at the same intensity level. The
reason for this problem can be explained using Fig. below where the pixels on
the horizontal line are placed one unit apart, whereas those on the diagonal
line are approximately 1.414 units apart. This difference in density produces
the perceived difference in brightness.
3. Picket Fence
Problem The picket
fence problem occurs when an object is not aligned with of does not fit into
the pixel grid properly. Fig. (a) below shows a picket fence where the distance
between two adjacent pickets is not a multiple of the unit distance between
pixels. Scan converting it normally into the image space will result in uneven
distances between pickets since the endpoints will have to be snapped to pixel
coordinates. This is sometimes called global aliasing, as the overall length of
the picket fence is approximately correct. On the other hand an attempt to
maintain equal spacing will greatly distort the overall length of the fence.
This is sometimes called local aliasing, as the distances between pickets are
kept close to their true distances. Another example of such a problem arises
with the outline font. Suppose we want to scan convert the uppercase character
E in Fig. below from its outline description to a bitmap consisting of pixels
inside the region defined by the outline. The result in Fig. exhibits both
asymmetry and dropout . A slight adjustment and / of realignment of the outline
can lead to a reasonable outcome.
Anti aliasing Techniques Most
aliasing artifacts, when appear in a static image at a moderate resolution are
often tolerable and in many cases, negligible, However, they can have a
significant impact on our viewing experience when lift untreated in a series of
images that animate moving objects. For example a line being rotated around one
of its endpoints becomes a rotating escalator with length altering steps. A
moving object with small parts of surface details may have some of those
features intermittently change shape of even disappear. Although
increasing image resolution is straightforward way to decrease the size of
many aliasing artifacts and alleviate their negative we pay a heavy
price in terms of system resource and the results are not always satisfactory.
On the other hand there are techniques that can greatly reduce aliasing
artifacts and improve the appearance of images without increasing their
resolution. These techniques are collectively referred to as anti aliasing
techniques. Some anti- aliasing techniques are designed to treat a particular
type of artifact. For instance, an outline font can be associated with a set of
rules or hints to guide the adjustment and realignment that is necessary for
its conversion into bitmaps of relatively low resolution. An example of such
approach is called the
True Type font.
1. Pre- filtering
and post-
Filtering Pre- filtering and post-filtering are two types of general-
purpose anti- aliasing techniques. The concept of filtering originates from the
field of signal processing, where true intensity values are continuous signals
that consist of elements of various frequencies. Constant intensity values that
correspond to a uniform region are at the low end of the spectrum. In order to
lessen the jagged appearance of lines and other contours in the image space, we
seek to smooth out sudden intensity changes, or in signal- processing terms, to
filter out the high frequency components. A pre- filtering techniques works on
the true signal in the continuous space to derive proper values for individual
pixels (filtering before sampling), whereas a post- filtering techniques takes
discrete samples of the continuous signal and uses the samples to compute pixel
values (sampling before filtering).
2. Area Sampling Area sampling is a pre- filtering
techniques in which we superimpose a pixel grid pattern onto the continuous
object, definition. For each pixel area that intersects the object, we
calculate the percentage of overlap by the object. This percentage determines
the proportion of the overall intensity values of the corresponding pixel that
is due to the object's contribution. In other words, the higher the percentage
of overlap, the greater influence the object has on the pixel's overall
intensity value. In fig. (a) a mathematical shown in dotted form is represented
by a rectangular region that is one pixel wide. The percentage of overlap
between the rectangle and each intersection pixel is calculated analytically.
Assuming that the background is black and the line is white, the percentage
values can be used directly to set the intensity of the pixel [see fig. (b)].
On the other hand, had the background been gray (0.5, 0.5, 0.5) and the line
green (0, 1, 0), each blank pixel in the grid would have had the background
gray value and each pixel filled with a fractional number F would have been
assigned a value of [0.5 (1-f), 0.5 (1-f) +f, (1-f)] a proportional blending of
the background and object colors. Although the resultant discrete approximation
of the line in Fig. (c) takes on a blurry appearance, it no longer exhibits the
sudden transition from an on pixel to an off pixel and vice versa, which is
what we would get with an ordinary scan- conversion method [see in fig.] . This
trade- off is characteristic of an anti- aliasing techniques based on supper
sampling.
3. Super Sampling In this approach we subdivide each
pixel into sub pixels and check the position of each sub pixel in relation to
the object to be scan- converted. The object's contribution to a pixel's
overall intensity value is proportional to the number of sub pixels that are
inside the area occupied by the object. Fig 5 shows a example where we have a
white object that is bounded by two slanted lines on a black background. We
subdivide each pixel into nine (3*3) subpixels. The scene is mapped to the
pixel values in Fig. (b). The pixel at the upper 7 right corner, for instance,
is assigned- since seven of its nine sub pixels are inside the object area. Had
the object been red (1,0,0) and the background light yellow (0.5, 0.5, 0), the
pixel would have been assigned (1*7/9+0.5*2/9,0.5),which is (8/9,1/9,0). Super
sampling is often regarded as a post filtering technique since discrete samples
are first taken and then used to calculate pixel values. On the other hand it
can be viewed as an approximation to the area sampling method since we are
simply using a finite number of values in each pixel area to approximate the
accurate analytical result.
4. Low pass
Filtering This is a
post filtering technique in which we reassign each pixel a new value that is a
weighted average of its original value and the original values of its
neighbors. A low pass filter in the form of a (2/I + 1) (2/I + 1) grid, where
n> 1, holds the weights for the computation. All weight values in a filter
should sum to one. To compute a new value for pixel, we align the filter with
the pixel grid and center it at the pixel. The weighted average is simply the
sum of products of each weight in the filter times the corresponding pixel
original value. The filter shown in fig. means that half of each pixel's
original value is retained in its new value, while each of the pixel four
immediate neighbors contributes one eighth of its original value . A low pass
filter with equal weights sometimes referred to as a box filter, is said to be
doing neighborhood averaging. On the other hand a filter with its weight values
conforming to a two dimensional is called a Gaussian
filter.
5. Pixel
Phasing Pixel phasing is a hardware
based anti aliasing technique. The graphics system in this case is capable of
shifting individual pixels from their normal positions to the pixel grid by
fraction of the unit distance between pixels. By moving pixels closer to the
true line of other contour this technique is very effective in smoothing out
the stair steps without reducing the sharpness of the edges.
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