This text covers topics such as ECG filters, spectrograms, and video quality metrics for compression. It includes sample code in MATLAB for signal processing and analysis. The text also provides solved assignments and essays on Digital Signal Processing and Video Compression.
ENGT5111 Digital Signal Processing Signal Analysis and Video Compression 2018 Student Name Student ID Number
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PROBLEM 1 y(n)=1 21(−2x[n]+3x[n−1]+6x[n−2]+7x[n−3]+6x[n−4]+3x[n−5]−2x[n−6]) Section 1 An electrocardiogram (ECG) defines a time-varying signal that demonstrates the ionic current flow which causes the cardiac fibers to contract and subsequently relax. The signals are collected using a device that measures and records the electrical activity of the heart from electrodes placed on specific points of a person’s skin. The signal is used by cardiologists to detect heart related illnesses such as the coronary artery disease, the left ventricular hypertrophy, kalemia, myocardial, valvular and congenial heart disease. The most common method uses 12 distinct points where the leads are placed.The ECG trace represents several electrical entities such as the P wave, the QRS complex, and the T wave. Some of the most common noises captured during the ECG recording are the baseline wander, power line interference, and muscle noise. The ECG filtery(n)aimsat removing the noise without altering the desired information in the biomedical ECG signal recorded. The linear phase filter maybe implemented as it avoids phase distortion that can alter various temporal relationships in the cardiac cycle. Some filters are ineffective when small knots are recorded
whereas performing apolyfit()affects the heart rate outlook. This particular filter is a nonlinear filter whose output is based on the input. TheECGsignalisrecordedasarepetitivesignalthatusesensembleaveraging techniques. The width functions especially for the low pass filter that have variable frequency response, the width function is designed to reflect local signal attribute that show smooth segments of the ECG. The ECG is filtered to remove errors. An ECG signal corrupted by muscle noise which causes the low amplitude waveforms which are obstructed during the recording of the ECG signal. It is quite difficult to remove it without affecting the ECG signal attributes since it overlaps with the complex PQRST. It is not in any way linked to the narrow band filtering. The muscle noise overlaps with actual ECG data recorded. Section 2 Design and implementation of a recursive filter for the ECG signal f=∑ m,n (f,ψm,n)ψm,n ϕ(x 2)=20.5 ∑ n h(n)ϕ(x−n) ψ(x 2)=20.5 ∑ n g(n)ϕ(x−n) dj+1(p)=∑ n g(n−2p)aj(n) aj+1(p)=∑ n h(n−2p)aj(n) T=σ√2logn
PRD= √∑ n=0 N (V(n)−VR(n)) 2 ∑ n=0 N V2(n) SNR=log10 ∑ n=0 N VR 2(n) ∑ n=0 N SR 2(n) It has shorter frequency response transition width despite having issues of system instability. Section 3 Expression of the output of the two filters given the input signal isx[n]=cos(0.35n) y[n]=1 21(−2x[n]+3x[n−1]+6x[n−2]+7x[n−3]+3x[n−5]−2x[n−6]) FreqS = 44.35;% Sampling frequency fcuts = [0.5 1.0 45 46];% Frequency Vector mags =[0 1 0];% Magnitude (Defines Passbands & Stopbands) devs = [0.050.010.05];% Allowable Deviations [n,Wn,beta,ftype] = kaiserord(fcuts,mags,devs,FreqS); n = n + rem(n,2); hh = fir1(n,Wn,ftype,kaiser(n+1,beta),'scale'); figure(1) freqz(hh, 1, 2^14, FreqS)
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PROBLEM 2 Section 1 Spectrogram in acoustic signals Acoustic signals capture human speech or any other form of sound. The acoustic signals are recorded based on the articulation. Speech is dynamic as it is affected by the pitch, loudness, quality and as a result it is transmitted at different frequencies over time. The speech filter varies over time as the speech is articulated. The sound signal is represented using an amplitude and time. The frequency response of the signal is obtained from the recorded continuous time signal. There are several ways to analyze and review the acoustic signal. The amplitude spectrum represents the amplitude and frequency albeit the time. The original form of the sound signal representation is using the time waveform. It can also be represented as an amplitude spectrum. The spectrogram combines the time, amplitude, and frequency attributes of a signal. All the components are equally represented. Good quality dynamic signal spectra are desired in real world applications. For instance, one would want to hear a good voice on radio as opposed to a distorted speech of very poor quality. The spectrogram is obtained when a sequence of spectra is stacked together in time and the amplitude axis is compressed. The system plots frequency against time while the amplitude is
demonstrated using a line illustration on the two axes. The graph demonstrates the energy contentwithblacksectionsillustratingthehighlyconcentratedregionswiththehighest amplitude while white shows the least concentrated regions or the noise floor. Any region that is represented between these extreme regions is the varying shades of grey. An acoustic signal is analyzed using a sound spectrograph which produces the spectrogram as the output. The spectrograph captures the dynamics of speech and may vary only in frequency, amplitude, and time. There are two types of spectrograms such as wide-band spectrogram and the narrow-band spectrogram. The wide band spectrogram has a short time spectrum calculation with damped analysis filter while the narrow band spectrogram produces an analytic scheme that emphasizes the change in the frequency of a signal. The spectrogram, therefore, represents how the frequency content of a signal changes with time. The table below compares the two types of signal spectrograms, Wide-band spectrogramsNarrow-band spectrograms 1.Bandwidthoftheanalyzingfilteris broad (300Hz) 1.It has a narrow bandwidth (45 Hz) 2.Compared to the spectrum envelopes2.Compared to the amplitude spectra 3.3. Section 2 Using a wav file to analyze the sound and signal spectrum. The wav file used in this case is the Grunta.wavfile. The speech or audio signal is a sound amplitude that varies in time. The wav file is a digital signal that is further analyzed using the signal spectrogram. The values can vary continuously or take from a discrete set, the time and space and can also be continuous or discrete. Using matlab signal to analyze the audio file, Digital signal processing %PART I %problem 1 clearall closeall
%problem 2 [y,fs]=audioread('Grunta.wav'); sound(y,fs); figure(1) plot(y) gridon title('Grunta Sound Signal') xlabel('Samples') ylabel('Continuous time sound signal') x=y(10000:15000) figure(2) plot(x) gridon title('Grunta Sound Signal') xlabel('Samples') ylabel('Continuous time sound signal') %Sampling the speech spectrogram figure(3) subplot(2,1,1) specgram(y) subplot(2,1,2) specgram(x,256,fs)%outputs the line spectra %PART II
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PART II (VIDEO PROCESSING) Question 1 There are benefits of computing theBjøntegaard-Delta (BD) metric instead of only comparing the operational rate-distortion curves of the two codecs. The tool evaluates the coding efficiency for the given video codec based on a range of quality points or bit rates. The video compression experiment shows the BD-cycle reduction when bypass grouping is used relative to AVC for different number of bypass bins per cycle; again the AVC encoded bit stream with the same number of bypass bins per cycle as the HEVC encoded bit stream is used as the anchor for the BD-cycle calculation. In HEVC, processing 16 bypass bins per cycle reduces the BD-cycle by 32.5% for all intra, 24.8% for low delay, and 27.0% for random access. Thus bypass grouping provides an additional BD-cycle reduction of 9.1% for all intra, 5.7% for low delay, and 7.3% for random access. These techniques give HEVC up to 31.1% BD-cycle reduction over AVC under common conditions. It should be noted that in the worst case, where bypass bins account for over 90 per cent of the total bins, cycle reduction of up to50 per cent is achieved [6]. In addition, it was shown that HEVC throughput can be increased significantly by processing more bypass bins per cycle. Based on the analysis, 4 to 8 bypass bins per cycle is likely to provide the best trade-off in terms of throughput improvement vs. area cost.
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Question 2 For each video quality metric (MOS and PSNR), find the values of DH and DL corresponding to Figure 5 in the cited paper [Section II-D of the articleT. K. Tanet al., "Video Quality Evaluation Methodology and Verification Testing of HEVC Compression Performance," inIEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 1, pp. 76-90, Jan. 2016.] DH=46.2 DL=39.4mbps MSE=∑ i=0 M−1 ∑ t=0 N−1 (I(i,t)−Id(i,t))2 PSNR=10log(2B−1)2 MSE PSNRw=6PSNRy+PSNRCB +PSNRCR 8 Question 3 % test Bjontegaard metric R1 = [686.760000000000;309.580000000000;157.110000000000;85.9500000000000]; R2 = [893.340000000000;407.800000000000;204.930000000000;112.750000000000]; PSNR1 = [40.2800000000000;37.1800000000000;34.2400000000000;31.4200000000000]; PSNR2 = [40.3900000000000;37.2100000000000;34.1700000000000;31.2400000000000];
∆Roverall≈10 1 DH−DL ∫ DL DH [^rB(D)−^rA(D)]dD−1 avgdiff=−1.1922 avgdiff=31.4244 REFERENCES [1].Zhang Li-Bao. Region of interest image coding using iwt and partial bit plane block shift for network applications. In Computer and Information Technology, 2005.CIT 2005. The Fifth International Conference on, pages 624 – 628, sept. 2005 [2].P. Artameeyanant. Image watermarking using adaptive tabu search. In ICCAS-SICE, 2009, pages 1941 –1944, aug. 2009. [3].Lijie Liu and Guoliang Fan. A new jpeg2000 region-of-interest image coding method: partial significant bit planes shift. Signal Processing Letters, IEEE, 10(2):35 – 38, feb 2003. [4].Strang G. and StrelaV., “Short WaveletsandMatrixDilationEquations,”IEEE Transactions Signal Processing, vol. 43, pp. 108-115, 2005. [5].Vetterli M. and Kovacevic J., Wavelets and Sub band Coding, Englewood Cliffs, Prentice Hall, 1995, http /cm.bell-labs.com/ who/ jelena/Book/ home.html. [6].Wiegand T., Sullivan G., Bjontegaard G., and Luthra A., “Overview of the H.264 / AVC Video Coding Standard,” IEEE Transactions on Circuits System Video Technology, pp. 243-250, 2003. [7].Wonkookim and Chung C., “On Preconditioning Multi wavelet System for Image Compression,”InternationalJournalofWaveletsMultiresolutionandInformation Processing, vol. 1, no. 1, pp. 51-74, 2003. [8].K. R. Namuduri and V. N. Ramaswamy, “Feature preserving image compression,” in Pattern Recognition Letters, vol. 24, no. 15, pp. 2767-2776, Nov. 2003. [9].B. E. Usevitch, “A Tutorial on Modern Lossy Wavelet Image Compression: Foundations of JPEG 2000,” in IEEE Signal processing Magazine, vol. 18, no. 5, pp. 22-35, Sep. 2001 [10].C. A. Gonzales, “DCT Coding of Motion Sequences Including Arithmetic Coder,” ISO/IEC JCT1/SC2/WP8, MPEG 89/187, MPEG 89/187, 2009.
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