CUSTOMER_CODE
SMUDE
DIVISION_CODE
SMUDE
EVENT_CODE
OCTOBER15
ASSESSMENT_CODE BC5902_OCTOBER15
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
6145
QUESTION_TEXT
Summarize the key difference between the GIF and JPEG image
formats?
SCHEME OF
EVALUATION
GIF
1.Better for clip art and drawn graphics with few colors or large
blocks of color
2.Can only have up to 256 colors
3.Images are “lossless” they contain the same amount of
information as the original
4.Can be animated
5.Can have transparent areas
JPEG
1.Better for photographs with lots of colors or fine color details
2.Can have up to 16 million colors
3.Images are “lossy” they contain less information than the original
4.Cannot be animated
5.Cannot have transparent areas
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
6147
QUESTION_TEXT
Explain LZW coding.
SCHEME OF
EVALUATION
The technique called Lempel-ziv- weich (LZW) coding, assigns fixedlength code words to variable – length sequences of source symbols but
requires no a priori knowledge of the probability of occurrence of the
symbols to be encoded. (1 mark)
LZW compression has been integrated into a variety of
mainstream imaging file formats including the graphic interchange
format (GIF),TIFF and PDF (1 mark)
At the onset of the coding process, a code book or “dictionary”
containing the source symbols to be coded is constructed. (1 mark)
For 8 –bit monochrome images, the first 256 words of dictionary are
assigned to the gray values 0,1,2,….255
As the encoder sequentially examines the images is a pixel gray level
sequences that are not in the dictionary are placed in algorithmically
determined locations. (1 mark)
If the first two pixels of the image are white, for instance, sequences
“255-255” might be assigned to the location 256, the address following
the locations are reserved for gray levels 0 through 255. (2 marks)
The next time that two consecutive white pixels are encountered, code
word 256, the address of the location containing sequence 255-255. Is
used to represent them.
The size of the dictionary is an important system parameter. (1 mark)
If it is too small the direction of matching gray level sequences will be
less likely: (1 mark)
If it is too large the size of the code words will adversely affect
compression performance. (1 mark)
An LZW decoder builds an identical decompression dictionary as it
decoded simultaneously, the encoded data stream. (1 mark)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
6148
QUESTION_TEXT
Explain the TIFF (Tagged Image File Format) image format.
SCHEME OF
EVALUATION
1.Tagged Image File Format (TIFF) is a variable-resolution bit mapped
image format developed by Aldus, (now part of Adobe) in 1986. TIFF is
very common for transporting color or gray-scale images into page
layout applications, but is less suited to delivering web content. (2
marks)
2.TIFF files are large and are of very high quality. Baseline TIFF images
are highly portable; most graphics, desktop publishing, and word
processing applications understand them. (1 mark)
3.The TIFF specification is readily extensible, though this comes at the
price of some of its portability. Many applications incorporate their own
extensions, but a number of application-independent extensions are
recognized by most programs. (2 marks)
4.Four types of baseline TIFF images are available: bi-level (black and
white), gray scale, palette (i.e. indexed), and RGB (i.e. true color). RGB
images may store up to 16.7 million colors. Palette and Gray-Scale
images are limited to 256 colors or shades. A common extension of TIFF
also allows for CMYK images. (2 marks)
5.TIFF files may or may not be compressed. A number of methods may
be used to compress TIPP files, including the Huffman and LZW
algorithms. Even compressed, TIFF files are usually much larger than
similar GIF of JPEG files. (2 marks)
6.Because the files are so large and because there are so many possible
variations of each TIFF file type, few web browsers can display them
without plug-ins. (1 mark)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72570
QUESTION_TEXT
Explain the types of digital images.
SCHEME OF
EVALUATION
1. GIF
2. JPEG
3. TIFF
4. PNG
1) GIF: It was developed in 1987. It is one of the most widely used
image formats on the web. It is recognized by gif file extension. It is
an 8 bit format, which means the maximum number of colors supported
by the format is 256. There are 2 GIF standards, 87 a and 89 a.
2) JPEG : It is a standardized image compression mechanism. it stands
for joint photographic experts group. It compresses either full-color or
gray scale images. It uses a lossey compression method. it was
developed for 2 reasons: It makes image files smaller and it stores 24bit per pixel color data instead of 8-bit per pixel data.
3) TIFF: Tagged image File format. TIFF files are large and of very high
quality
TIFF specification is readily extensible.
4 types of baseline TIFF images are available
a) bi-level
b)gray scale
c) Palette
d) RGB
TIFF files may or may not be compressed because the files are so large
and so many possible variations of each TIFF file type, few browsers can
display then without plug-ins
4) PNG: It is pronounced as in ping-pong; for portable N/W graphics.
It is superior to G/F in many ways,
following features are:
* Images that are the same size or slightly smaller than their GIP
counterparts.
* Support for indexed color, gray-scale & RGB
* Support for 2-D progressive rendering.
* An alpha channel that allows an image to have multiple levels of
opacity.
* Gamma correction
* File integrity checks.
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72572
Explain the basics of filtering in the frequency domain.
QUESTION_TEXT
The following steps are
1) Multiply the inputs image by (-1)x+y to center the transform.
2) compute F(U,V), the DFT of the image from (1)
SCHEME OF EVALUATION 3) Multiply F(U, V) by a filter function H(U,V)
4) Compute the inverse DFT of the result in (3)
5) Obtain the real part of the result in (4)
6) multiply the result in (5) by (-1)X+Y
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72574
QUESTION_TEXT
Explain the most popular Huffman technique for removing coding
redundancy.
SCHEME OF
EVALUATION
When coding the symbols of as information source individually,
Huffman coding yields the smallest possible number of code symbols
per source symbols. The resulting code is optimal for a fixed value of n,
subject to the constraint that the source symbols are to be coded one at
a time
The first step in Huffmans approach is tp create as a series of source
reductions by ordering the probabilities of the symbols under
consideration and combining the lowest probability symbols into a
single symbol that replaces them in the next source reduction.
The second step in Huffman’s procedure is to code each reduced
source, starting with the smallest source and working back to the
original source. The minimal length binary code for a two-symbol
source, of course, are the symbols 0 and 1. Huffman’s procedure
creates the optimal code for a set of symbols and probabilities subject
to the constraint that the symbols are to be coded one at a time. After
the code has been created, coding and /or decoding is accomplished in
a simple lookup table manner. The code itself is an instantaneous
uniquely decodable block code. It is called a block code because each
source symbol is mapped into a fixed sequence of code symbols.
Thus, any string of Huffman encoded symbols can be decoded by
examining the individual symbols of the string in a left to right manner.