Dip Answer Key Sep2010

8/3/2019 Dip Answer Key Sep2010

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Explain with neat block diagram thesteps involved in compressing a

video signal

10 marks question

Sep 2010


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Sep 2010

Discuss the following Simultaneous contrast 5 marks

Optical Illusion 5 marks


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10 Marks Sep 2010

What is image segmentation? Describe brieflythresholding and adaptive thresholding?(3+3+4)


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Introduction

Segmentation is a very important imageanalysis pre-processing step.

Note - in image analysis the inputs are imagesbut the outputs are attributes extracted fromthese images.


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More points

Segmentation is mostly based on rather adhoc methods.

Segmentation usually makes sense in a scopeof a particular application. Segmentation depends on an application, its

semantics. Methods are not universally applicable to all

images.


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V ideo On Fundamentals


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Image segmentation approach

Image segmentation algorithms are based on oneof the two basic properties of intensity values

discontinuity and similarity .

Discontinuity The assumption is that boundaries of regions are

sufficiently different from each other and from the

background to allow boundary detection based onlocal discontinuities in intensity. Edge-based segmentation is the principal approach used in thiscategory.


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C ontinued .

Similarity Region based segmentation approaches in the

second category are based on partitioning animage into regions that are similar according to aset of predefined criteria.


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Thresholding

Thresholding is a fundamental approach thatis quite popular when speed is an importantfactor.

Threshold-based, according to a global property,usually intensity, where the global knowledge isrepresented by the intensity histogram.


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H ow it works

Whereas the conventional thresholding operator uses aglobal threshold for all pixels, adaptive thresholdingchanges the threshold dynamically over the image. Thismore sophisticated version of thresholding can

accommodate changing lighting conditions in the image,e.g.those occurring as a result of a strong illuminationgradient or shadows.A daptive thresholding typically takes a grayscale or colorimage as input and, in the simplest implementation,outputs a binary imagerepresenting the segmentation. Foreach pixel in the image, a threshold has to be calculated. If the pixel value is below the threshold it is set to thebackground value, otherwise it assumes the foregroundvalue.


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Two main approaches to finding the threshold: (i) theC how and Kanenko approach and (ii) local thresholding.

Basis - The assumption behind both methodsis that smaller image regions are more likely tohave approximately uniform illumination, thusbeing more suitable for thresholding.


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(i) the C how and Kanenko approachand (ii) local thresholding

the C how and Kanenko approach the image is divided into an array of overlapping

sub images and then find the optimum thresholdfor each sub image by investigating its histogram.

local thresholding statistically examine the intensity values of the

local neighborhood of each pixel.


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10 marks

Explain JPEG compression


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JPEG C ompression(The steps include the D CT part in the next question, so read in continuation )

Lossless and lossy image compression

The JPEG process is a widely used form of

lossy image compression that centers aroundthe D C T


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JPEG C ompression process


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Subsampling

JPEG uses 4:2:0 subsampling scheme. This schemesubsamples in both the horizontal and vertical directions by afactor two. Theoretically. A n average chroma pixel ispositioned between the rows and columns


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Subsampling Y C rC b


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Sep 2010 10 marks

Discuss the working principle of D C T in imagecompression


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DC T - Introduction

The d iscr ete cosin e t ransf o rm (DC T) is a technique forconverting a signal into elementary frequencycomponents. It is widely used in image compressionA ll three of the following standards employ a basictechnique known as the discrete cosine transform(DC T). Developed by A hmed, Natarajan, and Rao[1974], the D C T is a close relative of the discreteFourier transform (DFT).

JPEG For compression of still images MPEG For compression of motion video H .261 - For compression of video telephony and

teleconferencing


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JPEG and D C T

Psychophysical experts suggests that humansare much less likely to notice the loss of veryhigh spatial frequency components than lower

frequency components.JPEG uses D C T to basically reduce the highfrequency contents and then efficiently codethe result into a bit string. A s frequencybecomes higher it becomes less importantand the coefficient may be set to zero.


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DC T

Each image is subdivided into 8x8 blocks and 2 D DCT is applied to each block image f( i,j),with output being the D C T co-efficients F (u,v)

for each block.


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The Two-Dimensional D C T

The one-dimensional D C T is useful inprocessing one-dimensional signals such asspeech waveforms. For analysis of two-dimensional (2D) signals such as images, weneed a 2D version of the D C T. For an n x mmatrix s, the 2D D C T is computed in a simple

way: The 1D D C T is applied to each row of sand then to each column of the result. Thus,the transform of s is given by


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The D C T Equation


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The D C T Matrix


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Doing the D C T on 8*8 block


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Performing D C T (Final Step)


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JPEG C ompression continue


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JPEG C ompression continue


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JPEG Compr e ssi on co n t inu e


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Sep 2010 10 marks

What are the differences between smoothingand sharpening filters? Explain with anexample


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Imag e e nhanc e m e n t s


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S m oot hing

Image sensors and transmission channels mayproduce certain type of noise characterized byrandom and isolated pixels with out-of-range graylevels, which are either much lower or higherthan the gray levels of the neighboring pixels. Thechallenge is to distinguish between the details(small features, edges, lines, etc.) in the imageand this type of isolated and out-of-range noise,

with the goal to keep the former whilesuppressing the latter.


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Smoothing Domain FiltersSmoothing Domain FiltersEdges and other sharp transitions (such as noise) in the gray levels of an imagecontribute significantly to the high frequency content of its Fourier transform. H encesmoothing (blurring) is achieved in the frequency domain by attenuating a specifiedrange of high frequency components in the transform of a given image. Low passfiltering would be part of preprocessing stage for an image analysis system lookingfor features in an image bank.

Low-pass filters reveal underlying two-dimensional waveform with a longwavelength or low frequency image contrast at the expense of higher spatialfrequencies.

Low-frequency information allows the identification of the background pattern,and produces an output image in which the detail has been smoothed or removed fromthe original. C hoosing the median valuefrom the moving window does a better job of suppressing noise and preserving edgesthan the mean filter [3-4].


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S harp e ning

A s the opposite of smoothing operations, imagesharpening has the goal to enhance the details (thehigh spatial frequency components) of the image.

A high-pass filtered image can be obtained as thedifference between the original image and its low-pass filtered version.


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Sharpening Domain FiltersImage sharpening can be achieved in the frequency domain by high pass filtering process.

Simply subtracting the low-frequency image resulting from a low pass filter from

the original image can enhance high spatial frequencies. H igh -frequency information

allows us either to isolate or to amplify the local detail. If the high-frequency detail is

amplified by adding back to the image some multiple of the high frequency

component extracted by the filter, then the result is a sharper, de-blurred image.

The above two approaches can be implemented in the following manner:

The Fourier transform of an image, as expressed by the amplitude spectrum is a

breakdown of the image into its frequency or scale components. Filtering of these

components use frequency domain filters that operate on the amplitude spectrum of an

image and remove, attenuate or amplify the amplitudes in specified wavebands. The

frequency domain can be represented as a 2-dimensional scatter plot known as a

Fourier spectrum, in which lower frequencies fall at the center and progressivelyhigher frequencies are plotted outward [3-6].

Filtering in the frequency domain consists of 3 steps:

1. Obtain the Fourier transform the original image and compute the Fourier spectrum.

2. Select appropriate filter transfer function and multiply by the elements of the

Fourier spectrum. ( H ere the transfer function is the standard Gaussian and

Butterworth filters as low and high pass filters with varying input arguments. The

filter order and value of sigma have been selected by default as 1 and 10 respectively).


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C ontinued .

Refer the slides 04 Enhancement - SpatialFiltering - C h3


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Sep 2010

Q 2B - What is morphological imageprocessing? Explain any two basic operations.(2+4+4)

A nswer Please refer the PDF 11 Mo rph olo gica l IP - Ch 9. p d f


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Sep 2010

Q 2 A - What is Bit Plane image slicing? Explainthe procedure of performing bit plane slicing?


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Sep 2010

1A What are intensity and spatial resolutionof an image? Explain.


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Spatial Resolution

Spatial resolution is a measure of the smallestdiscernible detail in an imag e .

Quantitatively, spatial resolution can be stated as Line pairs per unit distance Dots (pixels) per unit distance

Image resolution definition The largest number of discernible line pairs per unit

distance. In U.S. we call this as dpi (dots per inch) E.g. newspaper with resolution 75 dpi Book page 2400 dpi


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Spatial Resolution C ont

To be meaningful, measures of spatialresolution must be stated with respect tospatial units. Image size by itself doesn t tellthe complete story. To say that an image has,say, a resolution 1024 * 1024 pixels is not ameaningful statement without stating the

spatial dimensions encompassed by theimage.


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Intensity resolution

Intensity resolution similarly refers to thesmallest discernible change in in te nsi ty level.We have considerable discretion regarding thenumber of samples used to generate a digitalimage, but this is not true regarding thenumber of intensity levels. Based on hardwareconsiderations, the number of intensity levelsusually is a an integer power of two. The mostcommon number is 8 bits.


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Intensity resolution cont

Unlike spatial resolution, which must be based on a perunit of distance basis to be meaningful, it is commonpractice to refer to the number of bits used to quantizeintensity as the intensity resolution.For example, it is common to say that an image whoseintensity is quantized into 256 levels has 8 bits of intensity resolution.True discernible changes in intensity are influenced notonly by noise and saturation values but also by thecapabilities of human perception.


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Sep 2010

1B What is two dimensional Discrete FourierTransform? Why frequency domain imageanalysis is very important? Explain. (2+ 4+ 4)


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Why frequency domain image analysisis very important


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