Lecture 10: Tutorial Sakrapee (Paul) Paisitkriangkrai Evan Tan A/Prof. Jian Zhang

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1 Lecture : Tutorial Sakrapee (Paul) Paisitkriangkrai Evan Tan A/Prof. Jian Zhang NICTA & CSE UNSW COMP959 Multimedia Systems S 9 jzhang@cse.unsw.edu.au

2 Part I: Media Streaming Evan Tan Acknowledgement: Thanks to Reji Mathew for providing the original material

3 Q Consider a video-on-demand system designed for streaming video and audio content over the internet. What problems would you encounter, if you tried to stream the video and audio content directly over UDP/IP without using RTP? COMP959 Multimedia Systems Lecture 7 Slide J Zhang

4 Cannot detect lost packets Unable to reorder received packets Unable to do audio-video stream synchronization RTP provides: Pay load identification, and Frame indication to support applications / decoders COMP959 Multimedia Systems Lecture 7 Slide 4 J Zhang

5 Q Consider streaming of a live event from a video source (i.e. sender) to a client (i.e. receiver) using RTP/RTCP. After some time of successful operation, the video source ignores all RTCP receiver reports (RR) from the client. What problems could arise if all RR are ignored by the video source? COMP959 Multimedia Systems Lecture 7 Slide 5 J Zhang

6 Not enough information to adapt to network RTCP Receiver Report (RR) Sent by receivers (not active senders) Provides a report of reception statistics COMP959 Multimedia Systems Lecture 7 Slide 6 J Zhang

7 Q What functionality does RTSP provide? What transport protocol is specified (if any) for RTSP? COMP959 Multimedia Systems Lecture 7 Slide 7 J Zhang

8 Establishes and controls one or more continuous media streams Provides Internet VCR controls Transport-independent COMP959 Multimedia Systems Lecture 7 Slide 8 J Zhang

9 Q4 In a peer-to-peer video conferencing situation what tools or techniques are available to achieve better resilience to packet loss? COMP959 Multimedia Systems Lecture 7 Slide 9 J Zhang

10 Receiver Error concealment Fill in missing blocks COMP959 Multimedia Systems Lecture 7 Slide J Zhang

11 Sender Adaptive rate control Packetization strategy Error resilience increase intra-frames intra-block refresh introduce redundancies COMP959 Multimedia Systems Lecture 7 Slide J Zhang

12 Part II: Multimedia Information Retrieval Sakrapee (Paul) Paisitkriangkrai

13 COMP959 Multimedia Systems Lecture 7 Slide J Zhang Tutorial Question Consider an image with distinct grey-levels Calculate the co-occurrence matrix if the position operator is defined as one pixel to the left

14 Revision on Texture The Grey-level Co-occurrence Matrix (GLCM) can be used to calculate the second-order statistics. Given the following x pixel image with distinct greylevels: And the position operator defined as one pixel to the left COMP959 Multimedia Systems Lecture 7 Slide 4 J Zhang

15 Revision on co-occurrence matrix The x co-occurrence matrix can be calculated as follows:- Gray-Levels at left Gray-Levels at current pel N N COMP959 Multimedia Systems Lecture 7 Slide 5 J Zhang N N N = the number of pixels with grey-level that have a gray-level of one pixel to the left N = the number of pixels with grey-level that have a gray-level of one pixel to the left N = the number of pixels with grey-level that have a gray-level of one pixel to the left N = the number of pixels with grey-level that have a gray-level of one pixel to the left

16 COMP959 Multimedia Systems Lecture 7 Slide 6 J Zhang Revision on co-occurrence matrix Position Operator (One pixel to the left) can not be defined Current Pixel Grey-level of with grey level of one pixel to the left

17 Revision on co-occurrence matrix Grey-level of with grey level of one pixel to the left Current Pixel Hence, the final co-occurrence matrix will be equal to Gray-Levels at current pel Gray-Levels at left COMP959 Multimedia Systems Lecture 7 Slide 7 J Zhang

18 Tutorial Question Step Step The size of the co-occurrence matrix will be equal to the number of distinct grey-levels x the number of distinct grey-levels (L x L) Since, the image has distinct grey-levels, the size of the co-occurrence matrix is x. Gray-Levels at current pel Gray-Levels at left COMP959 Multimedia Systems Lecture 7 Slide 8 J Zhang

19 Tutorial Question Step Step Count the number of pixels in the image which have the relationship between itself and its neighbors specified by position operator. The position operator in our question is defined as one pixel to the left N = the number of pixels with grey-level that have a gray-level of one pixel to the left = COMP959 Multimedia Systems Lecture 7 Slide 9 J Zhang

20 Tutorial Question Step Step Count the number of pixels in the image which have the relationship between itself and its neighbors specified by position operator. The position operator in our question is defined as one pixel to the left N = the number of pixels with grey-level that have a gray-level of one pixel to the left = 8 COMP959 Multimedia Systems Lecture 7 Slide J Zhang

21 Tutorial Question Step Step Count the number of pixels in the image which have the relationship between itself and its neighbors specified by position operator. The position operator in our question is defined as one pixel to the left N = the number of pixels with grey-level that have a gray-level of one pixel to the left = 8 COMP959 Multimedia Systems Lecture 7 Slide J Zhang

22 Tutorial Question Step Step Count the number of pixels in the image which have the relationship between itself and its neighbors specified by position operator. The position operator in our question is defined as one pixel to the left N = the number of pixels with grey-level that have a gray-level of one pixel to the left = 4 COMP959 Multimedia Systems Lecture 7 Slide J Zhang

23 Tutorial Question Step Step Fill in the answers calculated from Step Hence, the final co-occurrence matrix will be:- Gray-Levels at current pel Gray-Levels at left COMP959 Multimedia Systems Lecture 7 Slide J Zhang

24 COMP959 Multimedia Systems Lecture 7 Slide 4 J Zhang Tutorial Question Consider two images A and B, and histogram similarity matrix C A B C For each image, calculate histogram, cumulative histogram and CCV For the two images, calculate L histogram distance, L cumulative histogram distance, histogram intersection, Normalized CCV distance and Niblack s histogram similarity value. Assume that average filtering has already been applied to the image. Suppose that the threshold for the size of the connected component is.

25 Revision - Histogram A histogram is a graphical display of tabulated frequencies. Grey- Level Frequenc y COMP959 Multimedia Systems Lecture 7 Slide 5 J Zhang

26 Revision - Histogram A histogram is a graphical display of tabulated frequencies. Grey- Level Frequenc y 8 COMP959 Multimedia Systems Lecture 7 Slide 6 J Zhang

27 Revision - Histogram A histogram is a graphical display of tabulated frequencies. Grey- Level No. of Observations 8 6 COMP959 Multimedia Systems Lecture 7 Slide 7 J Zhang

28 Revision - Histogram A histogram is a graphical display of tabulated frequencies. Grey- Level Frequency Frequency 6 Grey- Level of Grey- Level of Grey- Level of COMP959 Multimedia Systems Lecture 7 Slide 8 J Zhang

29 COMP959 Multimedia Systems Lecture 7 Slide 9 J Zhang Tutorial Question - Histogram 6 8 Frequency Grey-Level Frequency Grey-Level Image B Image A Histogram of Image A Histogram of Image B

30 Revision Cumulative Histogram A cumulative histogram is a mapping that counts the cumulative number of observations in all of the bins up to the specified bin. Grey-Level Histogram No. of Observations Grey-Level Cumulative Histogram No. of Observations 8 +8 = = 5 COMP959 Multimedia Systems Lecture 7 Slide J Zhang

31 Tutorial Question Cumulative Histogram Histogram of image A Cumulative Histogram of image A Histogram of image B Grey- Frequency Level 8 6 Grey- Frequency Level Grey- Frequency Level 9 5 Grey- Frequency Level Cumulative Histogram of image B COMP959 Multimedia Systems Lecture 7 Slide J Zhang

32 Revision - Niblack s Similarity Measurement COMP959 Multimedia Systems Lecture 7 Slide J Zhang

33 Revision Color Coherence Vector Color coherence vector (CCV) is a tool to distinguish images whose color histograms are indistinguishable. Coherence measure classifies pixels as either coherent or incoherent. 4 Steps to compute CCV Step: conduct average filtering Step: discretize the image into n distinct colors Step: Classify the pixels within a given color bucket as either coherent or incoherent a pixel is coherent if the size of the connected component exceeds a fixed value. Step4: Obtain CCV by collecting the information of both coherent and incoherent. COMP959 Multimedia Systems Lecture 7 Slide J Zhang

34 Revision CCV (Step,) Step Since average filter has already been applied, this step will be skipped. Step Discretize the image We discretize both images into three distinct colors COMP959 Multimedia Systems Lecture 7 Slide 4 J Zhang

35 COMP959 Multimedia Systems Lecture 7 Slide 5 J Zhang Revision CCV (Step ) Step Classify the pixels as either coherent or incoherent. Discretized Image E E A C A E F A C A E E A A A D E A B A D D A B A Connected Component 6 Size Color F E D C B A Label Connected Table

36 Revision CCV (Step ) Step Classify the pixels as either coherent or incoherent A pixel is coherent if the size of the connected component exceeds a fixed value of ; otherwise, the pixel is incoherent. Label A B C D E F Color Color Size 6 α β Connected Table Color Coherent Vector COMP959 Multimedia Systems Lecture 7 Slide 6 J Zhang

37 Revision CCV (Step ) Step Classify the pixels as either coherent or incoherent A pixel is coherent if the size of the connected component exceeds a fixed value of ; otherwise, the pixel is incoherent. Label A B C D E F Color Color Size 6 α β 6 Connected Table Color Coherent Vector COMP959 Multimedia Systems Lecture 7 Slide 7 J Zhang

38 Revision CCV (Step ) Step Classify the pixels as either coherent or incoherent A pixel is coherent if the size of the connected component exceeds a fixed value of ; otherwise, the pixel is incoherent. Label A B C D E F Color Color Size 6 α β 6 Connected Table Color Coherent Vector COMP959 Multimedia Systems Lecture 7 Slide 8 J Zhang

39 COMP959 Multimedia Systems Lecture 7 Slide 9 J Zhang Tutorial Question - CCV Image B Image A β 6 α Color Color Coherent Vector of Image A β α Color Color Coherent Vector of Image B

40 Tutorial Question Bin Bin Histogram 8 6 Histogram Cumulative Histogram 9 5 Cumulative Histogram CCV α β 6 CCV α β Image A Image B COMP959 Multimedia Systems Lecture 7 Slide 4 J Zhang

41 Tutorial Question L Histogram Distance D = = 4 L Cumulative Histogram Distance D = = 4 Histogram Intersection D = [min(,9) + min(8,8) + min(6,8)] / ( ) = [ ] / 5 = / 5 =.9 COMP959 Multimedia Systems Lecture 7 Slide 4 J Zhang

42 Tutorial Question Normalized CCV D = (-8)/(+8+) + (-)/(++) + (6-6)/(6+6+) + (-)/(++) + (-8)/(+8+) + (-)/(++) = / + / +5/ =.87 Niblack s similarity measure Transpose(Z) = [ -9, 8-8, 6-8 ] = [,, ] T D = Z CZ.5 = [ ].5.5 [ ] =.5 8 COMP959 Multimedia Systems Lecture 7 Slide 4 J Zhang

43 Tutorial Question Consider the boundary and the numbering schemes Digitize the boundary Select the red node as starting point, calculate the chain code in counter-clockwise direction Calculate the normalized chain code by using the first difference of the chain code COMP959 Multimedia Systems Lecture 7 Slide 4 J Zhang

44 Tutorial Question Revision on Chain Code Represent a boundary by a connected sequence of straight-line segments of specified length and direction Based on 4- or 8- connectivity Depends on the starting point and the spacing of the sampling grid COMP959 Multimedia Systems Lecture 7 Slide 44 J Zhang

45 Tutorial Question Revision on Chain Code Steps to calculate chain code Digitize image Decide a sampling gird, the denser the more accurate Assign boundary point to grid node based on the distance of the grid node to the boundary, d < threshold assign boundary point to the node Select a numbering scheme and define a starting point Follow the boundary in a specified direction (clockwise or counter clockwise), assign a direction to the segments connecting neighboring boundary points d COMP959 Multimedia Systems Lecture 7 Slide 45 J Zhang

46 Tutorial Question Steps to solve question. Digitize input boundary Straightforward since the sampling grid and distance threshold are given as the rectangular grid and the dotted circles COMP959 Multimedia Systems Lecture 7 Slide 46 J Zhang

47 Tutorial Question Steps to solve question. Boundary following is a bit more complex for 4-connectivity (starting point given as the red point, direction given as counter clockwise) Some ancillary boundary points have to be added The chain code is COMP959 Multimedia Systems Lecture 7 Slide 47 J Zhang

48 Tutorial Question Steps to solve question The adding of ancillary points should be consistent with the direction of boundary following COMP959 Multimedia Systems Lecture 7 Slide 48 J Zhang

49 Tutorial Question Steps to solve question. Normalize for rotation by using the first difference of the 4-direction chain code The difference is obtained by counting the number of direction changes that separate two adjacent elements of the code. The chain code is The first difference of the chain code is COMP959 Multimedia Systems Lecture 7 Slide 49 J Zhang

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