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Reducing Complexity in Signal Processing Algorithms for Communication Receiver and Image Display Software

Wireless Networking and Communications Group. Reducing Complexity in Signal Processing Algorithms for Communication Receiver and Image Display Software. Brian L. Evans Prof. Brian L. Evans. Seminar at the American University of Beirut. 27 July 2010. Outline. Embedded digital systems

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Reducing Complexity in Signal Processing Algorithms for Communication Receiver and Image Display Software

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  1. Wireless Networking and Communications Group Reducing Complexity inSignal Processing Algorithms forCommunication Receiver andImage Display Software Brian L. Evans Prof. Brian L. Evans Seminar at the American University of Beirut 27 July 2010

  2. Outline • Embedded digital systems • Generating sinusoidal waveforms • Discrete-time filters • Multicarrier equalizers • Image halftoning algorithms • Conclusion 2004 2005 2006 2007 2008 2009 2010

  3. Embedded Digital Systems • Often work on application-specific tasks • In consumer products (2008 units) 1200M cell phones 70M DSL modems 300M PCs 55M cars/light trucks 100M digital cameras 30M gaming consoles (2007) 100M DVD players • iPhone has six programmable processors (2008) • Embedded programmable processors Inexpensive with small area and volume Predictable off-chip input/output (I/O) rates “Low” power (TI C5504 45mW @ 300MHz) Limited on-chip memory Fixed-point arithmetic

  4. Embedded Digital Systems • Memory access in processors External I/O: block data transfers to/from on-chip memory Internal I/O: on-chip memory to CPU registers using data buses (e.g. TI C6000 processor has two 32-bit data buses) • Common word sizes for signal processing software 64-bit floating-point for desktop computing (e.g. Matlab) 32-bit floating-point for pro-audio and sonar beamforming 16-bit fixed-point for speech, consumer audio, image proc. • IEEE floating-point operations Handles many special cases (e.g. +∞, -∞ and not a number) Add, multiply, divide have comparable hardware complexity

  5. Embedded Digital Systems • Fixed-pointoperations Multiplicationbased on addition operations Division takes 1-2instructions perbit of accuracy Multiplication canconsume muchdynamic power • Truncate constantsfor power savings 56% Multiplier used in TI C64 processors [Han, Evans & Swartzlander, 2005]

  6. Generating Sinusoidal Waveforms • Sample continuous-time cosine signal at rate fs Discrete-time fixed frequency 0 = 2 f0 / fs Example: f0 = 1200 Hz and fs = 8000 Hz, 0 = 3/10  Discrete-time realization drops fs term in front of cosine • Math library call to cos function in C Uses double-precision floating-point arithmetic No standard in C for internal implementation Generally meant for high-accuracy desktop calculations • Call to gsl_sf_cos_e in GNU scientific library 1.8 20 multiply, 30 add, 2 divide, 2 power calculations/output

  7. Generating Sinusoidal Waveforms • Difference equation with input x[n] and output y[n] y[n] = (2 cos 0) y[n-1] - y[n-2] + x[n] - (cos 0) x[n-1] From inverse z-transform of z-transform of cos(0n) u[n] Impulse response gives cos(0n) u[n] 2 multiplications and 3 adds per output value Buildup in error as n increases due to feedback • Lookup table – pre-compute samples offline Discrete-time frequency 0 = 2 f0 / fs = 2 N / L All common factors between integers N and L removed  = 2  k = 2 (N / L) n → n = L → store L samples Entries in either floating-point or fixed-point format Table would contain N periods of the cosine Initial conditions are all zero

  8. Generating Sinusoidal Waveforms • Signal quality vs. implementation complexity in generating cos(0n) u[n] with 0 = 2 N / L MAC Multiplication-accumulationRAM Random Access Memory (writeable) ROM Read-Only Memory

  9. Discrete-Time Filters • Finite impulse response (FIR) filter • Impulse response h[k] has finite extent k = 0,…, M-1 x[k-1] x[k] z-1 z-1 … z-1 … h[0] h[1] h[2] h[M-1] S y[k] Discrete-time convolution

  10. v[k] x[k]   b0 y[k] UnitDelay a1 b1 v[k-1] UnitDelay v[k-2] a2 b2 Discrete-Time Filters • Infinite impulse response (IIR) filter Biquad building block: 2 poles and 0-2 zeros Generally, coefficients a1, a2, b0, b1, b2 are real-valued Biquad is short for biquadratic− transfer function is ratio of two quadratic polynomials

  11. Discrete-Time Filters (1) For same piecewise constant magnitude specification(2) Algorithm to estimate minimum order for Parks-McClellan algorithm by Kaiser may be off by 10%. Search for minimum order is often needed.(3) Algorithms can tune design to implementation target to minimize risk

  12. Discrete-Time Filters • Keep roots computed by filter design algorithms Polynomial deflation (rooting) reliable in floating-point Polynomial inflation (expansion) may degrade roots • Choice of IIR filter structure matters Direct form IIR structures expand zeros and poles, and may become unstable for large order filters (order > 12) Cascade of biquads expands zeros and poles in each biquad • Minimum order design not always most efficient Efficiency depends on target implementation Consider power-of-two coefficient design Efficient designs may require search of ∞ design space

  13. Halftime: AUB Summer 2005 • EECE 503 Real-Time DSP Lab • Embedded digital systems • Generating sinusoidal waveforms • Discrete-time filters • Multicarrier equalizers • Image halftoning algorithms • Conclusion

  14. Channel Equalization • Channel degrades transmitted signal Nonlinear distortion, e.g. amplitude nonlinearities Linear distortion, e.g. convolution by channel impulse response Additive noise, e.g. thermal (Gaussian) and impulsive • Equalization compensates linear distortion Spreading/attenuation in time Magnitude/phase distortion in frequency Received bit stream Message bit stream Transmitter Channel Receiver Equalizer

  15. Multicarrier Modulation • Divide channel into narrowband subchannels • Discrete multitone modulation Baseband transmission based on fast Fourier transform (FFT) Each subchannel carries single-carrier transmission Standardized for digital subscriber line (DSL) communication channel carrier magnitude subchannel frequency Subchannels are 4.3 kHz wide in DSL systems

  16. Channel Equalization nk Channel Equalizer • Equalizer Shortens channelimpulse response(time domain eq.) Compensates phase/magnitude distortion(freq. domain eq.) • Single carrier system – g is scalar constant FIR filter w performs time and frequency domain equalization • Multicarrier system – g is FIR filter of length n+1 Time domain equalizer (w) then FFT & freq. domain equalizer yk xk rk ek w h + + + Training signal - Ideal Channel Receiver generates xk g z- Discretized Baseband System Equalization in DSL receivers increases bit rate by 10x

  17. Multicarrier Equalization • Maximum shortening SNR time domain equalizer Minimize energy leakage outside shortened channel length For each position of window  [Melsa, Younce & Rohrs, 1996] • Cholesky decomposition of Bleads to optimal eigensolution Computationally-intensive: O(Lw3) Floating-point multiplications/divisions Restricts TEQ length to be less than n+1 n+1 samples channel impulse response effective channel impulse response

  18. Time Domain Equalizer Design Bit Rate (Mbps) TEQ length of 17 Data rates averaged over eight standard DSL test lines [Martin et al., 2006] Training complexity in log10(multiply-add operations) Most efficient floating-point versions of algorithms used

  19. Time Domain Equalizer Design • Unified framework [Martin et al., 2006] A and B are square (LwLw) and depend on choice of  Constraint prevents trivial non-practical solution w = 0 • Find eigenvector for largest generalized eigenvalue Formulation Power method Alternating Lagrangian Iterative Methods division-free 20 iterations to converge for 17-tap MSSNR TEQ design

  20. Original Image Threshold at Mid-Gray x(m) b(m) Digital Image Halftoning • For display on devices with fewer bits ofgray/color resolution than original image Grayscale: 8-bit image to 1-bit image Color: 24-bit RGB image to 12-bit RGB display • Produces artifacts Each pixel in original image is 8-bit unsigned intensity in [0, 255] For display, 0 is black and 255 is white

  21. Consider 4-bit data on 2-bit display (unsigned) Feedback quantization error For constant input 1001 = 9 Average output value ¼ (10+10+10+11) = 1001 4-bit resolution at DC ! Noise shaping Truncating from 4 to 2 bits increases noise by ~12dB Feedback removes noise at DC & increases HF noise Inputsignal words 4 2 Todisplaydevice 2 2 1 sample delay Quantization with Feedback Adder Inputs OutputTime Upper Lower Sum to display 1 1001 00 1001 10 2 1001 01 1010 10 3 1001 10 1011 10 4 1001 11 1100 11 Added noise 12 dB (2 bits) Periodic f

  22. Original 7/16 3/16 5/16 1/16 Halftone Spectrum Halftone Error Diffusion Halftoning • Quantize each pixel • Diffuse filtered quantization error to “future” pixels difference threshold u(m) x(m) b(m) current pixel _ + _ + e(m) [Floyd & Steinberg, 1976] compute error shape error error filter weights

  23. Error Diffusion Halftoning • Deterministic bit flipping quantizer (DBF)[Damera-Venkata & Evans, 2001] Thresholds input to black (0) or white (255) Flip quantized value about mid-gray (128) Reduces false textures in mid-grays Implemented with two comparisons DBF(x) 255 x1 128 x2 x

  24. Sharpness Control Signal transfer function models sharpening Ks ≈ 2 for Floyd-Steinberg Noise transfer function models noise-shaping Kn = 1 • Model quantizer as gain plus noise [Kite, Evans & Bovik, 1997] Ks = 2 2 1 w w -w1 w1 -w1 w1 Pass high frequency noise Pass low and enhance high frequencies Plots for ideal lowpass H()

  25. Sharpness Control • Adjust by threshold modulation [Eschbach & Knox, 1991] Scale image by gain L and add it to quantizer input • Flatten signal transfer function [Kite, Evans & Bovik, 2000] L b(m) u(m) x(m) _ + _ + e(m)

  26. Results Floyd-Steinberg Original DBF quantizer Unsharpened

  27. Conclusion • Processor architecture Decrease data sizes to reduce on-chip memory usage and increase data bus efficiency Truncate multiplicand constants to reduce power • Compute signal values by recursion or lookup table • Algorithm design Keep offline design results in full precision until end Order of calculations matters in implementation Exploit problem structure in developing fixed-point algorithms Linearize nonlinear systems to leverage linear system methods • Many other ways to reduce complexity exist

  28. Invitations • Panel discussion on graduate studies Tomorrow (Wednesday) 1:30 – 2:30 pm in this room (RCR) Panelists: Prof. Zaher Dawy (AUB), Prof. Imad El-Hajj (AUB) and Prof. Brian Evans (UT Austin) • IEEE Workshop on Signal Processing Systems Early October 2011 Short walk from the AUB campus Organizers include Prof. Magdy Bayoumi (Univ. of Louisiana at Lafayette), Prof. Brian Evans (UT Austin), Dean Ibrahim Hajj (AUB) and Prof. Mohammad Mansour (AUB)

  29. Thank You!

  30. AnnualRevenue Share Digital Signal Processors DSP Processor Market • Market ~1/3 of $25B embedded digital signal processing market 2007 cholesterol loweringPzifer Lipitor sales: $13B • Applications (2007) Source: Forward Concepts Source: Forward Concepts

  31. Introduction Screening (Masking) Methods • Periodic thresholds to binarize image Periodic application leads to aliasing (gridding effect) Clustered dot screening is more resistant to ink spread Dispersed dot screening has higher spatial resolution Blue larger masks (e.g. 1” by 1”) Clustered dot mask Dispersed dot mask index Threshold Lookup Table

  32. Ks Linear Gain Model for Quantizer • Extend sigma-delta modulation analysis to 2-D Linear gain model for quantizer in 1-D [Ardalan and Paulos, 1988] Linear gain model for grayscale image [Kite, Evans, Bovik, 1997] • Error diffusion is modeled as linear, shift-invariant Signal transfer function (STF): quantizer acts as scalar gain Noise transfer function (NTF): quantizer acts as additive noise { us(m) Ks us(m) Signal Path u(m) b(m) n(m) un(m) un(m) + n(m) Noise Path

  33. Original Image Threshold at Mid-Gray Dispersed Dot Screening Clustered DotScreening Stucki Error Diffusion Floyd SteinbergError Diffusion Spatial Domain

  34. Dispersed Dot Screening Threshold at Mid-Gray Original Image Clustered DotScreening Stucki Error Diffusion Floyd SteinbergError Diffusion Magnitude Spectra

  35. Human Visual System Modeling • Contrast at particular spatialfrequency for visibility Bandpass: non-dimbackgrounds[Manos & Sakrison, 1974; 1978] Lowpass: high-luminance officesettings with low-contrast images[Georgeson & G. Sullivan, 1975] Exponential decay[Näsäsen, 1984] Modified lowpass version[e.g. J. Sullivan, Ray & Miller, 1990] Angular dependence: cosinefunction[Sullivan, Miller & Pios, 1993]

  36. Image Floyd Stucki Jarvis Analysis and Modeling barbara 2.01 3.62 3.76 boats 1.98 4.28 4.93 lena 2.09 4.49 5.32 mandrill 2.03 3.38 3.45 Average 2.03 3.94 4.37 Linear Gain Model for Quantizer • Best linear fit for Ks between quantizer input u(m) and halftone b(m) Stable for Floyd-Steinberg Can use average value to estimate Ks from only error filter • Sharpening: proportional to Ks [Kite, Evans & Bovik, 2000] Value of Ks: Floyd Steinberg < Stucki < Jarvis • Weighted SNR using unsharpened halftone Floyd-Steinberg > Stucki > Jarvis at all viewing distances

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