A eswrum o; ELEGI men PROGRESS
`
`4sta®qas IEEE TRANSACTIONS ON
`COMMUNICATIONS
`
`JULY 1984
`
`VOLUME COM-32
`
`NUMBER 7
`
`(ISSN 0090'6778I
`
`A PUBLICATION OF THE IEEE COMMUNICATIONS SOCIETY
`//—\\
`II?“\
`I
`IEEE
`\’ I/
`\
`/.-
`"\\If ,x” J}
`
`PAPERS
`
`Cmmnunit'alion Theory
`On Optimum and Nearly Optimum Data Quantization for Signal Detection ..... B. Atizlmng and H. V. Poor
`On Miary DPSK Transmission Over Terrestrial and Satellite Channels ..... R. F. Pawn/a
`Performance of Portable Radio Telephone Using Spread Spectrum ..... K. Yammla, K. Dal/Ink“. and H. (/xm'
`
`'l‘onns'ley, and
`
`. .M. G. Gouda and Y.-T. Yu
`
`Forthcoming Topics of IEEE Journal on Select
`
`Cummmzit'afimz Theory
`Preemphasis/Deemphasis Effect on the Output SNR of SSB—FM ..... E. K. Al-Hussaini and EM. El-Rub/‘llt’
`Decimations of the Frank-Heimiller Sequences ..... W. 0. Allin/7
`A Two-Power—Level Method for Multiple Access Frequency-Hopped Spread-Spectrum Communication
`......l. J. Melsnt'r
`
`Computer Communications
`Random Multiple—Access Communication and Group Testing ..... T. Berger. N. Mc/zrumri. D.
`J. Wolf
`Synthesis of Communicating Finite State Machines with Guaranteed Progress. .
`
`.
`
`Dam Calm;iunlt'ali'mz Systems
`Network Design for a Large Class of Teleconferencing Systems ..... M. J. Ferguson and L. Mumn
`
`Satellite and Space Cmnmunit'alion
`Interference Cancellation System for Satellite Communication Earth Station ..... '1‘. KuiIsI/ku and T. lnnuc
`Unique Word Detection in TDMA: Acquisition and Retention... . .5. S. Kcunul and R. G. Lyons
`TSI—OQPSK for Multiple Carrier Satellite Systems ..... H. lem Van and K. FC/It’)‘
`
`Signal PI'Uc‘eA'sirig and Cammunit‘utiun Elet'li‘t);iit'.s‘
`Noise Reduction in Image Sequences Using Motion—Compensated Temporal Filtering. .
`S. Sabri
`
`.
`
`.
`
`.L’. Du/mis and
`
`The Ett‘ectiveness and Efficiency of Hybrid Transform/DPCM Interframe Image Coding ..... W. A. Pear/mun
`and P. Jnkulu’ur
`
`CONCISE PAPERS
`
`Signal Processing and Communication Electronics
`Multiplierless Implementations of MF/DTMF Receivers ..... R. C. Agnru‘al. R. Smllmkar. and B. P. Agruu'ul
`
`CORRESPONDENCE
`
`Signal Proces‘s‘ing and COIN/Hlllllt’tll‘lUII Electronics
`Hamming Coding of DCT-Compressed Images Over Noisy Channels ..... D. R. (‘omstmk and J. D. Gibson
`A One—Stage Look—Ahead Algorithm for Delta Modulators ..... N. Solicinbcrg. E. Perla.
`.I. Bur-bu. and
`I). L. Schilling
`
`Page 1 of 9
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`GOOGLE EXHIBIT 1007
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` A CENTURY OF ELECTRICAL PROGRESS
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`IEEE COMMUNICATIONS SOCIETY
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`IEEE
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`The field of interest of the IEEE Communications Society consists of all telecommunications including telephone. telegraphy. facsimile.
`and point-to-point television. by electromagnetic
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`propagation including radio; wire: aerial. underground. coaxial. and submarine cables; waveguides. communication satellites. anti 1
`users; in marine. aeronautical. space and fixed station services;
`repeaters radio relaying. signal storage. and regeneration; telecommunication error detection and correction: multiplexmg and carrier techni
`qucs: communication switching systems; data
`communications: and communication theory.
`In addition to the above, this TRANSACTIONS contains papers pertaining to analog and digital signal processing and modulation, audio and video encoding techniques. the theory and design of
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`transmitters. receivers. and repeaters for communications via optical and sonic media. the destgn and analysis of computer communication systems. and the development of communication
`software. Contributions of theory enhancing the understanding of communication systems and techniques are included.
`'
`‘
`‘
`-ii'e discussions of the social implications of the development of
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`IEEE TRANSACTIONS
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`Page 2 of 9
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`Page 2 of 9
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`

`

`
`
`IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. COM—32, NO. 7, JULY 1984
`
`745
`
`On Optimum and Nearly Optimum Data Quantization for
`Signal Detection
`13“" onyx
`
`Abstract—The application of companding approximation theory to the
`quantization of data for detection of coherent signals in a noisy environ-
`ment
`is considered. This application is twofold, allowing for greater
`simplicity in both analysis and design of quantizers for detection systems.
`Most computational methods for designing optimum (most efficient)
`quantizers for signal detection systems are iterative and are extremely
`sensitive to initial conditions. Companding approximation theory is used
`here to obtain suitable initial conditions for this problem. Furthermore,
`the companding approximation idea is applied to design suboptimum
`quantizers which are nearly as efficient as optimum quantizers when the
`number of levels is large. In this design, iteration is not needed to derive the
`parameters of the quantizer, and the design procedure is very simple. In
`this paper, we explore this approach numerically and demonstrate its
`effectiveness for designing and analyzing quantizers in detection systems.
`
`I. INTRODUCTION
`
`N recent years there have been several studies of problems
`relating to the quantization of data for use in signal detec-
`tion systems [l]—[6]. These studies include both analytical
`and numerical
`treatment of
`the problem of optimal data
`quantization for the detection of deterministic (coherent)
`signals [1], [2] and purely stochastic signals [5] , and analyti-
`cal treatments of quantization within more general signal de-
`tection formulations [3]—[6]. In particular, Kassarn [1] has
`considered this problem for the coherent detection case and
`has developed a design technique for this situation based on
`the solution to a system of nonlinear equations in the quan-
`tizer parameters. He showed that quantizers derived in this
`manner have maximum efficacy (i.e., are most efficient)
`among all quantizers with a fixed number of output levels.
`It
`is
`interesting to compare Kassam’s quantizer
`to those
`optimized by a criterion not specifically for signal detection
`purposes; for instance, the minimum-distortion quantizer [7],
`which minimizes the mean-squared error between data and its
`quantized version, coincides with the optimum quantizer
`based on Kassam’s detection criterion [1] only for Gauss-
`ian noise.
`In the alternative context of quantizing data for minimum
`distortion, approximations to the minimum-distortion non—
`uniform quantizer which are of practical interest have been
`proposed. Bennett [8] modeled a nonuniform quantizer by a
`compressor, followed by a uniform quantizer and an expander
`(compander). With this companding model, Panter and Bite
`[9] presented a useful approximation to minimum-distortion
`quantizers. Later, Algazi [10] used the companding approxi—
`mation to obtain results on optimal quantizers for a general
`class of error criteria (Algazi estimated distortion due to
`
`Paper approved by the Editor for Communication Theory of the IEEE
`Communications Society for publication after presentation at the Conference
`on Information Sciences and Systems, Johns Hopkins University, Baltimore.
`MD, March 1983. Manuscript received March 22, 1983; revised November
`21, 1983. This work was supported by the Joint Services Electronics Program
`(US. Army, Navy, and Air Force) under Contract N00014—79-C~0424.
`The authors are with the Department of Electrical Engineering and the
`Coordinated Science Laboratory, University of Illinois at Urbana-Champaign,
`Urbana, IL 61801.
`
`"‘t- 1...:
`
`optimally quantizing data in the minimum mean-squared error
`sense; see also Gersho [1 1]).
`In this paper, we apply the companding approximation
`theory to signal detection problems. First, we use the com-
`panding approximation to help in solving Kassam’s system of
`nonlinear equations for the optimum quantizer parameters
`(see also Bucklew and Gallagher [12]). Then, we present a
`scheme to design a quantizer which in a sense estimates Kas-
`sam’s optimum quantizer using a companding approximation.
`The performance of detection systems using these companding
`quantizers is compared to that of Kassam’s optimum quantizer
`detector. Also, the exact performance of the optimum system
`is compared to its approximate performance predicted by the
`companding model. These issues are explored numerically for
`a wide range of noise distributions, including both Gaussian
`and non—Gaussian cases.
`
`II. PRELIMINARIES
`
`The model we consider is based on a standard additive noise
`assumption. In particular, we assume that we have a sequence
`of data samples x = {x5 1' = 1, 2, W, n} from a random se—
`quence X : {X13 1' = l, 2, W, n} which can obey one of the
`two possible statistical hypotheses:
`
`HOZXi:NiJ
`versus
`
`i=1,2,"',n
`
`(21)
`
`leXi=Ni+ 9.53,
`
`i= 1, 2,”, n
`
`where {N13 i = l, 2, ..., n} is an independent, identically dis—
`tributed (i.i.d.) zero—mean noise sequence with known com—
`mon univariate probability density and distribution functions
`f and F, respectively. Throughout this work, the noise proba-
`bility density function fis assumed to be symmetric about the
`origin. The parameter 0 is a positive signal-to-noise ratio (SNR)
`parameter and {s,-;
`i = l, 2, m, n} is a known coherent (i.e.,
`deterministic) signal sequence. As a practical case, we wish
`to consider the weak signal case (0 *> 0+), since this is the
`situation in which the design is most critical. Therefore, rather
`than maximizing the detection probability (6) for a fixed false
`alarm probability (or), we consider the locally optimum de-
`tector for H0 versus H1 which maximizes the slope of the
`power function (asap/2x9) at (9 = 0 while keeping a fixed false-
`alarm probability. Within mild regularity conditions,
`the
`locally optimum test statistic for our detection problem is
`given by
`
`.n
`
`W: 25223100“)
`i=1
`
`where the locally optimum nonlinearity glob) is given by
`
`
`f'IIXJ
`gromé— ,
`f0)
`
`(22)
`
`(2.3
`
`)
`
`0090—6778/84/0700-0745$01.00 © 1984 IEEE
`
`
`
`Page 3 of 9
`
`Page 3 of 9
`
`

`

`
`
`826
`
`IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. COM-32, NO. 7, JULY 1984
`
`Noise Reduction in Image Sequences Using
`Motion-Compensated Temporal Filtering
`
`ERIC DUBOIS, MEMBER, IEEE, AND SHAKER SABRI, MEMBER, IEEE
`
`Abstract—Noise in television signals degrades both the image quality
`and the performance of image coding algorithms. This paper describes a
`nonlinear temporal filtering algorithm using motion compensation for
`reducing noise in image sequences. A specific implementation for NTSC
`composite television signals is described, and simulation results on several
`video sequences are presented. This approach is shown to be successful in
`improving image quality and also improving the efficiency of subsequent
`image coding operations.
`
`I. INTRODUCTION
`
`NOISE introduced in television signals degrades both the
`image quality and the performance of subsequent image
`coding operations. This noise may arise in the initial signal
`generation and handling operations, or in the storage or trans-
`mission of these signals. The effect of additive noise on poten—
`tial image coding performance is illustrated by considering a
`uniformly distributed noise with values —1. 0,
`1 out of 256,
`giving an SNR of 45.8 dB. Although this added noise is barely
`perceptible, it has an entropy of 1.58 bits/sample, clearly limit-
`ing the image coding compression factor. Thus, there is great
`interest in reducing the noise level in the input signal in order
`to get maximum coding efficiency.
`Noise reduction in image sequences is possible to the ex—
`tent that image and noise components have different character-
`istics. For stationary random processes, the classical method
`of noise reduction is Wiener filtering, based on the image
`and noise power spectra. However, images are not well modeled
`by stationary random processes, and other approaches based on
`improved image models are sought. A major distinguishing fea-
`ture between the noise and signal in image sequences is that the
`noise is uncorrelated from frame to frame, while the image
`is highly correlated, especially in the direction of motion.
`By performing a low-pass temporal filtering in the direction
`of motion, the noise component can be attenuated without
`affecting the signal component.
`Noise reduction using temporal filtering to give improved
`image quality has been described in [11—[3]. These systems
`use motion detection,
`as opposed to motion estimation;
`temporal filtering is only applied in the nonchanging parts
`of
`the picture. This
`is accomplished either by explicitly
`segmenting into changing and nonchanging areas, or by a non»
`linear
`filtering approach (to be discussed later). These al-
`gorithms have the disadvantage that noise cannot be reduced in
`moving areas without modifying the image detail, and noise
`can appear and disappear as objects begin and stop moving.
`Although noise in moving areas is masked to some extent by
`the motion, it will still be visible in slowly moving areas.
`
`Paper approved by the Editor for Signal Processing and Communication
`Electronics of the IEEE Commnications Society for publication without oral
`presentation. Manuscript received September 26, 1983. This work was sup-
`ported by the International Telecommunications Satellite Organization (IN-
`TELSAT) under Contract INTEL«114, 1980.
`E. Dubois is with INRS—Te‘lécommunications, Universite' du Quebec, Ile
`des Soeurs, Verdun, P.Q.. Canada H3E 1H6.
`S. Sabri is with Bell—Northem Research,
`lle des Soeurs, Verdun, P.Q.,
`Canada H3F. 1H6.
`
`filtering
`The concept of motion-compensated temporal
`has been described by Huang and Hsu [4]. In this approach,
`the displacement at each picture element
`is estimated, and
`a
`temporal averaging is performed along the trajectory of
`motion. Reference
`[4]
`describes nonrecursive linear and
`median temporal
`filters, both with and without motion
`compensation. However, the amount of noise reduction which
`can be attained with low-order nonrecursive filters is quite
`limited. Also this approach can introduce artifacts in areas
`where motion is not tracked and in newly exposed areas.
`In this paper, the nonlinear recursive filtering approach of
`[2], [3] is extended by the application of motion compensa—
`tion techniques. A specific noise reducer for use with NTSC
`composite television signals is then described, and computer
`simulation results of its performance on several Video se-
`quences are presented. It is shown that this approach is success-
`ful
`in improving image quality, while also improving the
`performance of subsequent image coding operations.
`
`11. MOTION -COMPENSATED TEMPORAL FILTERS FOR
`NOISE REDUCTION
`
`A. Theory ofMorion-Compensated Temporal Filtering
`Let u(x,
`,2) be the image intensity at spatial location x =
`(x1, x2) and time t, and let d(x, 2‘) be the displacement of the
`image point at (x, t) between time t — T and t, The vector
`field d(x,
`2‘)
`is called the displacement field. If the intensity
`of the object point has not changed over the time T, then
`
`u(x, t) = u(x— d(x, 1), r — T),
`
`(1)
`
`Note that d is not defined in newly exposed areas, i.e., for
`those picture elements (pels) which were not visible in the
`
`previous field. For background and stationary objects,d(x, t) =
`0, while for an object
`in translational motion, d(x, t) is a
`constant over the object. In general, d(x, r) is a slowly vary-
`ing function of space, except for discontinuities at the edges
`of moving objects.
`The value over time of the image sequence at a given object
`point
`forms a one-dimensional signal, defined on the time
`interval for which this point is visible in the scene. This Signal
`is assumed to be the sum of an image component and an ad-
`ditive noise component. The variation in the image compo—
`nent is solely due to change in the luminance of the object
`point, caused by changes in illumination or orientation of the
`object. This change is relatively slow, so that the image compo—
`nent
`is a low bandwidth signal. The noise is assumed to be
`white and uncorrelated with the signal. By performing a low-
`pass filtering operation on this signal. the noise component
`can be significantly attenuated, with a minimal effect on the
`image component.
`
`the image sequence is sampled spatially, and
`In practice,
`it is not precisely possible to filter the sequences correspond-
`ing to given object points. However,
`the principle of per-
`forming a temporal filtering or averaging operation along the
`trajectory of motion is feasible. This filtering can be of either
`the recursive or nonrecursive type. Since greater selectivity
`can be obtained for a given filter order with recursive filters,
`
`0090-6778/84/0700~0826$01.00 ©1984IEEE
`
`
`
`
`
`
`
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`
`DUBOIS AND SABRI: NOISE REDUCTION IN IMAGE SEQUENCES
`
`827
`
`this type of filter has been chosen for this application. This
`is especially important
`in temporal filtering, where each in—
`crease by one in filter order requires an additional frame
`memory.
`A block diagram of a first—order recursive temporal filter
`with motion compensation is shown in Fig.
`l
`(assume for
`now that the output of the block NL is a constant value or).
`The basic operation of this filter is described by
`
`u(x, t) = ocu(x, t) + (l — d)U(x — d(x, 2‘), t — T)
`
`(2)
`
`where v is the output of the filter, dis an estimate of d, and
`5 is an estimate of U at a non—grid point obtained by spatial
`interpolation. The signal 12(x, t) = 17(x — d,
`t — T) is called
`the prediction and e 2 u — 12
`is called the prediction error.
`This filter requires a frame memory in order to be able to
`form the prediction. A module for estimating the displacement
`field is also required. This estimation can be performed using
`any of a number of algorithms which have been proposed
`in the literature [5] -[8]. The displacement estimator can
`use the input signal as well as any of the signals available in
`the noise reducer to perform the estimate.
`An indication of the ability of this filter to reduce noise
`can be obtained by considering its performance in stationary
`areas Where d = 0. In this case, the filter reduces to a standard
`one—dimensional
`temporal recursive filter with transfer func-
`tion
`
`or
`
`(3)
`
`11(2) =
`
`1—(1—a)z—1'
`It can easily be shown that for a white noise input, the noise
`power is reduced by 10 log10((2 — 00/01) dB. Due to the
`spatial interpolation error, the performance in moving areas
`will be slightly different, even if the displacement estimate is
`perfectly accurate.
`A number of modifications are required to make this scheme
`work in practice The major change is based on the observa-
`tion that the displacement field is not defined for the newly
`exposed parts of the image, and that the displacement estimate
`may not always be accurate, especially in regions where
`(l) is violated. These regions are characterized by a large
`value of prediction error. Since the movement is not being
`followed in these regions, it is preferable to disable the filtering
`operation. This can be accomplished by varying the value of
`a as a function of the prediction error, which is equivalent
`to passing the prediction error 8 through a memoryless non-
`linearity y = a(e)'e. A typical piecewise-linear characteris-
`tic for the function 046) is shown in Fig. 2. It is given by
`
`i
`i
`_
`.‘
`
`,
`
`.
`:
`
`Gib,
`“In—Ole
`oz(e)= “kl +Pbae—Peab,
`Pb_Pe
`
`ifle|<Pb;
`
`ibe<Iel<Pe;
`
`“e,
`
`iflel>Pe.
`
`(4)
`
`In areas where the motion is tracked, e(x, t) is small (of the
`order of the noise level), and a linear temporal filtering with
`parameter or = 01b is performed. In areas where the motion is
`not being tracked and e(x, r) is large, a temporal filtering with
`parameter are
`is performed. To avoid introducing artifacts
`in these regions, are is typically set to unity. For values of e
`between Pb and P8, 01(e) varies linearly between orb and Ole,
`to provide a smooth transition between regions where motion
`is tracked and where it
`is not. The choice of values of Pb
`and Fe to be used depends on the noise level and the appearance
`of artifacts.
`
` 9
`
`
`Displacement
`Estimator
`
`
`Fig. l.
`
`First—order recursive temporal filter with motion compensation.
`
`ode)
`
`“e
`
`orb
`
`duI:
`.2
`.2
`cUC
`.2uN
`.2
`.3E:i
`E
`
`Pb
`
`Pe
`Prediction Error
`
`e
`
`Fig. 2. Nonlinear function for multiplier coefficient 02.
`
`The digital noise reducers which have been described in
`the literature [ll—[3] are basically obtained by setting the
`displacement estimate to zero, and filtering only in the sta-
`tionary areas. This can be accomplished by explicitly seg—
`menting into changed and unchanged areas, and filtering
`with a linear temporal filter in the unchanged areas, or by
`using a nonlinear
`temporal
`filter with the nonlinearity as
`described above.
`In either case, the noise in the moving or
`changed areas can only be reduced at the expense of image
`detail. (Note that higher noise level in changing areas is per—
`missible to a certain extent because the movement or change
`will mask the noise). With this system, noise can abruptly
`appear in areas which were fixed and then begin to move.
`If an accurate displacement estimate is available, these effects
`can be reduced. Clearly, a displacement estimator which is
`robust in the presence of noise is required.
`
`B. A Motion-Compensated Noise Reducer for NTSC
`Composite Video Signals
`
`temporal
`a particular nonlinear
`section describes
`This
`filter with motion compensation suitable for noise reduction
`in NTSC composite video signals. This noise reducer must
`specifically account for the properties of the NTSC composite
`signal, namely, the modulation of the chrominance informa-
`tion on a subcarrier, and the 2:1 line-interlaced scanning. The
`issues related to displacement estimation and prediction from
`NTSC composite signals are discussed in [8]. The techniques
`described here can easily be adapted to component processing
`of color video signals.
`The NTSC Signal: The NTSC signal has the form
`
`U0) 2 Y(r) + C0) = Y(r) + 1(r) cos (ZTrfscI + 33°)
`
`+ Q(r)sin(21rfsct + 33°)
`
`(5)
`
`where Y is the luminance component and I and Q are the
`chrominance components, quadrature-modulated on a sub-
`
`
`¥
`
`Page 5 of 9
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`

`
`
`N4.1:.5.
`
`a
`8°8
`
`IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. COM—32, NO. 7, JULY 1984
`
`frequency fSC = 3.579545 MHZ. The composite
`carrier at
`signal
`is sampled orthogonally at a frequency 4f“, in phase
`with the I component. givitka the. spatiotemporal sampling
`pattern shown in Fig. 3. With respect to the coordinate system
`of Fig. 3. the sampled composite signal has the form
`
`nlrr
`_
`"
`Lin) = Yin) + [(11) cos
`‘
`(—1) 2
`
`c M _ "7
`+ Quz) sin
`n
`t
`1)
`-
`
`’
`(6)
`
`where n = (111.123.1245l.
`In order
`to perform the prediction and displacement
`it
`estimation.
`is necessary to separate the composite signal
`P into luminance and chrominance components Y and C.
`This can be accomplished by using a two-dimensional digital
`filter [8]. 1n the present system. the chrominance component
`is separated from the composite siunal with a separable zero»
`phase FIR bandpass filter having impulse response
`
`4 0 —2
`haul. 113} = — -2 0
`lo
`l
`0 -2 0
`l
`
`.
`
`(7)
`'
`
`The luminance is obtained by subtracting the chrominance
`component from the composite signal.
`Prediction: The role of the predictor is to estimate the value
`of the composite signal at the current pel from previous fields.
`given the disptacement estimate. This estimate is gven by
`
`(“tax n = its: r) ~:- (1.1: n
`
`[8)
`
`\\ here (1x. ’) = iftx, :3 or :th. '1 Thus. the predictor must
`demodulate the output composite signal. an; estimate the
`
`x2
`
`X2
`
`Fig.3. Orthogonal sampung structure for NTSC signals.
`
`the output I. is separated into component's 2‘0 and C0:
`
`V43Separation
`
`10—201 l
`
`
`(9)
`
`I'm: :1 = You“ :\ ~l- (my, :1
`t
`x
`.
`,
`
`»» Ti.
`\\ here (4‘ = :10 or :90. Taen lta‘
`, nix , d. :
`\‘ “9:“ F“ ’5 “Emmi“ 1701?:- 10 b."
`“31 bilmear
`
`taterpoimon. tux :‘1 = :Letx « l
`ere [d].
`
`sampiing point
`displacement
`
`
`
`
`
`
`tc t.e:: low benomttzn ext. respect
`to their
`
`
`3:33ij by x..
`
`Fig.4. Blockdiagramofmotion—compensatedptedictor.
`
`area pels are skipped). Define the displaced frame dijjrérence
`
`Dtx, r. d) = “(xx 1) — unt- — d. r— T).
`
`(10)
`
`The goal of the_ displacement estimator is to choose 4 50 35
`to make Dt.\‘,-. Ldfl as small as possible. This is done recursively
`using the algorithm of steepest descent. gin'ng [e]
`l
`A
`k
`.7 -
`-
`‘
`‘
`.
`A
`-
`(I; — d,
`c1)t.\,~.!.d,~ In ut.\i— d]; 1. z — Ti.
`
`(11)
`
`l
`
`lt‘ltas been shown that the estimator can be gre {IS simplifiedr
`Without significantly affecting performance. as follows [6] 3
`A
`
`d- —’
`’
`
`.
`d’
`
`‘
`
`.
`.
`‘
`.
`\ o
`( Mg“ “KAI“ " ‘Ii 1“ “ii“ Kri‘W—dt—le” — I»
`(12)
`
`
`
`{er ottterence
`
`to give good results
`The scan urtlet which has been t‘oumi
`inns have,
`_
`
`lh‘l
`is a taster scan of sutxhlmkg of 5;“ _\‘\pels‘b\' )1 lines.
`In Image se-
`a o; the slew l‘lte initial displacement! estimate for the block is the estimate
`
`btei‘fd‘w‘e‘t
`for “W “N ml
`in the cortespomlina block in the previous
`x; N‘ the ith
`field. This scanning timer is illustrated‘ in Fig 5_ As mentione