0018-9162/05/$20.00 © 2005 IEEE46 Computer

C O V E R F E A T U R E

PP uu bb ll ii ss hh ee dd bb yy tt hh ee II EE EE EE CC oo mm pp uu tt ee rr SS oo cc ii ee tt yy

Computer-GeneratedHolography as a Generic DisplayTechnology

I nvented in 1947 by Dennis Gabor, hologra-phy—from the Greek holos, for whole—is a3D display technique that involves using inter-ference and diffraction to record and recon-struct optical wavefronts. Holography’s unique

ability to generate accurately both the amplitudeand phase of light waves enables applicationsbeyond those limited by the light manipulationcapabilities of lens- or mirror-based systems.

Computer-generated holography is an emergingtechnology, made possible by increasingly powerfulcomputers, that avoids the interferometric recordingstep in conventional hologram formation. Instead, asFigure 1 shows, a computer calculates a holographicfringe pattern that it then uses to set the optical prop-erties of a spatial light modulator, such as a liquidcrystal microdisplay. The SLM then diffracts the read-out light wave, in a manner similar to a standard holo-gram, to yield the desired optical wavefront.

Compared to conventional holographic ap-proaches, CGH

• does not rely on the availability of specializedholographic recording materials;

• can synthesize optical wavefronts without hav-ing to record a physical manifestation ofthem—for example, it can generate 3D imagesof nonexistent objects; and

• offers unprecedented wavefront control bymaking it easy to store, manipulate, transmit,and replicate holographic data.

Although CGH-based display systems can bebuilt today, their high cost makes them impracticalfor many applications. However, as compute powerand optical hardware costs decrease, CGH displayswill become a viable alternative in the near future.

ADVANTAGES CGH provides flexible control of light, making

it suitable for a wide range of display types, includ-ing 2D, stereoscopic, autostereoscopic, volumetric,and true 3D imaging. CGH-based display technol-ogy can produce systems with unique characteris-tics impossible to achieve with conventionalapproaches.

Optical efficiencyUsing a pure-phase analog SLM, it is possible to

diffract nearly all light falling onto the SLM intothe desired image. Conventional displays are oftenhighly inefficient because they use some form ofabsorption or attenuation to vary pixels’ gray lev-els. In addition, all the light falling onto a computer-generated hologram can be diffracted to any pixelof the image, resulting in very high dynamic ranges.

Computer-generated holography is a powerful technology suitable for awide range of display types, including 2D, stereoscopic, autostereoscopic,volumetric, and true 3D imaging. Although CGH-based display systems arecurrently too expensive for many applications, they will become a viablealternative in the near future.

Chris SlingerColinCameronMaurice StanleyQinetiQ

August 2005 47

Dynamic range in conventional displays is limitedas each of the display’s pixels is unambiguouslymapped to a corresponding pixel in the image.

Ease of tilingCGH pixels need not be contiguous. For exam-

ple, high-pixel-count CGH-based displays can bemade by assembling multiple microdisplay SLMsinto a 2D array. Gaps between the SLMs, neces-sitated by interface and power connections, arenot visible in the image projected from such com-puter-generated holograms. In conventional dis-plays, pixel-level continuity and accuracy arerequired to prevent the eye from seeing tiling arti-facts.

Tolerance of pixel defectsCGH delocalizes information associated with

any point in a 2D or 3D image and spreads it overthe hologram surface. Consequently, “dead” pixelsor even multiple rows or columns of defective pix-els do not cause noticeable degradation in theimage. In contrast, even small numbers of dead pix-els are unacceptable in conventional displays, amajor factor in production costs.

Wide color gamut CGH generally requires replay using lasers,

which, though expensive now, are becoming ubiq-uitous and decreasing in price. A major advantageof lasers is that their monochromaticity, or spectralpurity, yields a very wide color gamut, approaching90 percent of the color range the human eye canperceive. Most existing display systems deliver onlyabout 40 percent of the color gamut or less, as theyuse relatively broadband color filters.

Full depth cues Able to generate a wide range of optical wave-

fronts, CGH is the only technology capable of syn-thesizing a true 3D image—that is, one with all the

human visual system’s depth cues. Stereoscopic,autostereoscopic, and volumetric displays are defi-cient in some depth cues, or worse, produce con-flicting cues. This can result in the viewer ex-periencing discomfort, nausea, and other negativeeffects, especially after long-term use.

Conflicting depth cues also inhibit performanceof various tasks in non-CGH-based displays. In astereoscopic (glasses-based) display system, con-vergence and accommodation (focusing) depth cuesconflict.1 Viewer-position-independent obscurationis impossible in volumetric displays. Even advancedmultiview autostereoscopic systems, such as inte-gral imaging systems, generate optical wavefrontapproximations and resolutions significantly belowthe eye’s visual acuity capabilities.

High system volume and image resolutionThe lower resolutions of most display technolo-

gies, which use geometric optics for ray-basedapproximation, result from their need to minimizediffraction effects. In contrast, holographic systemsdirectly exploit diffractive effects and therefore canbe more compact as well as deliver images withhigher information content. Appropriate CGHalgorithms can adjust image resolution dynamically,allowing users to tailor system computing resourcesfor specific activities.

Artifact-free binary modulationDisplays, particularly microdisplays, are often

binary in nature. Examples include Texas Instru-ments’ Digital Micromirror Device, various ferro-electric liquid crystal microdisplays, and SiliconLight Machines’ Grating Light Valve technology. Togenerate images perceived as having good gray scale,these devices run at high frame rates and use tem-poral multiplexing. In some applications, this canresult in image degradation and artifacts. CGH,however, can produce gray-scale images from binaryfringe patterns, thus no multiplexing is necessary,

Figure 1. Computer-generated holography. A computer calculatesa holographic fringepattern for displayby the spatial lightmodulator (SLM),which diffracts laserlight to yield aninteractive, true 3D image.

Input: Laserwavefront Output: Diffracted

wavefront

SLM displaying holographicfringe pattern

Control and interaction via voice, gesture, haptics

3D image

Viewer(s)

Computer

48 Computer

hardware frame rates can be reduced, and tempo-ral dither artifacts are absent.

Aberration, distortion, and conformal correction

It is sometimes possible to precompensate theCGH pattern to prevent known optical aberrationsor imperfections from degrading CGH-generatedimages. In the case of projecting a 2D image ontoan arbitrary (but known or determinable) 3D sur-face, a CGH-based projector system can likewiseproduce a compensated, sharply focused image.

CHALLENGESDespite the unique capabilities of CGH-based dis-

play technology, it remains prohibitively expensivefor most commercial applications, largely becauseof the enormous pixel counts required to achievesufficient image resolution and image size (I). In thecase of direct-view CGH-based displays, generat-ing images with an adequate field of view (FOV)also adversely impacts pixel-count requirements.

Image width and field of viewFor direct-view CGH-based displays, the product

of I and FOV is the primary influence on CGHpixel counts.

To understand why, consider Figure 2, whichshows a typical Fourier transform configurationused to generate usable I × FOV irrespective ofCGH pixel spacing. The angle θ over which lightcan be diffracted from a hologram fringe pattern isgoverned by the grating equation

Λ sinθ = λ,

where λ is the replay light wavelength and Λ is thespatial frequency of the fringe diffracting the light.The largest value of θ is given when Λ = 2p, wherep is the pixel spacing on the SLM displaying theCGH pattern. Regardless of the optical elements’apertures and powers, I × FOV is proportional tothe number of pixels along the appropriate CGHaxis. Even for a relatively undemanding worksta-tion application with, say, I = 0.5 m and FOV =60º, a full-parallax computer-generated hologram

requires approximately 1012 pixels. Using techniques such as restricting the hologram

to horizontal parallax only—likely to be accept-able in many, but not all, applications—can reducesystem pixel-count requirements to about 1010.However, this is still three orders of magnitudehigher than the pixel counts of other high-end dis-play systems. The inability to calculate hologramfringe patterns or update an SLM system with thismany pixels, at interactive rates, remain major bot-tlenecks to widespread CGH adoption.

Image resolutionFor both 2D and 3D displays, the number of

resolvable image points NI in any plane of theimage perpendicular to the optical axis is directlyrelated to the computer-generated hologram’s pixelcount NCGH. In fact, NCGH NI, with typically NCGH

= 2NI. For many display applications, meeting thisconstraint is less onerous than a large I × FOV.However, for 2D projection systems, in which FOVis not a factor due to the scattering of light over awide range of angles by the projection screen sur-face, image resolution is the primary influence onsystem complexity.

Image qualityThe image from a computer-generated hologram

cannot contain more information than the holo-gram itself. Ingemar Cox and Colin Sheppard2 havedefined and quantified the channel capacity C ofan optical system. In the case of a CGH-basedimage generation system,

C nx log[Q] ny log[Q] log(1+S/N),

where Q is the number of quantization levels, nx,y

are the resolvable pixels across the hologramand/or image, and S/N is the signal-to-noise ratio.Thus, binary or other highly quantized hologramfringe patterns, while maintaining sufficient I ×FOV, can compromise image quality by decreas-ing the amount of information passing through thesystem. System designers must therefore pay care-ful attention to image-resolution, size, field-of-view,and signal-to-noise tradeoffs.

∝

≥

θSLMpixel spacing pnx × ny pixels

Fourier transformingoptics

Diffraction angle

ff

3D image

ViewingvolumeFOV

z

x

θ

Figure 2. Relationshipbetween image size( I) and field of view( FOV) in true 3D floating-image generation. I × FOVis proportional tothe number of pixelsacross the SLM, andis independent ofthe focal length ( f) of any optics. Thesame relationshipholds for otherreplay geometries.

Laser speckleUnlike conventional volume holograms, com-

puter-generated holograms must be illuminated(replayed) with narrowband and spatially coher-ent light—generally from red, green, and bluelasers—to produce color images. In addition to thehigh costs associated with this still-maturing tech-nology, particularly green lasers, speckle canadversely affect image quality. Designers can over-come this problem through various speckle-reduc-tion techniques, such as controlling laser systemcoherence.3 In addition, CGH can generate animage of a surface that appears diffuse without hav-ing to rapidly vary the optical wavefront’s phasestructure, which will reduce perceived speckle.

ALGORITHMS FOR CGH DESIGN Due to the daunting pixel counts that CGH-

based display systems require, special attentionmust be paid to the efficiency of the algorithms usedto calculate the holographic fringe patterns.4 Thesefringe patterns, represented as a 2D array of pixelvalues, are usually analog in form. Some CGHmodulators, such as QinetiQ’s Active Tiling sys-tem, require converting the analog pixel array dis-tribution into a binary distribution, with as littledegradation in the replayed 3D image as possible.Minimizing any additional computational load ofsuch a binarization step is particularly importantfor highly interactive systems.5

For all algorithms, the information for 3D imagegeneration is typically exported from a commercial3D computer-aided design tool as a triangulatedmesh with material properties. The material prop-erties can include diffuse, specular, and shininesscomponents.

Object surface intensities are based on user-defined material and light properties. The algo-rithms populate the mesh with points at a densityhigh enough that a human observer cannot seethem. The display system then holographicallyreconstructs these points. For simplicity we focuson full-parallax, true 3D image generation, al-though all approaches can be modified to producehorizontal-parallax-only holograms, with signifi-cant savings in computational resources.

Ping-pong algorithmOne of the best-known and simplest Fourier-

based CGH algorithms is the ping-pong techniquedevised by Yoshiki Ichioka, Masaharu Izumi, andTatsuro Suzuki.6 Building upon the crude first Bornapproximation, or superposition, approach, thistechnique introduces an obscuration operator that

enables the reconstructed 3D images toexhibit hidden-line-removal effects.

The ping-pong algorithm first slices theobject into planes perpendicular to the designplane. It then propagates light through theimage’s first plane (plane 1), toward thedesign plane, until it meets the next plane(plane 2). Next, the algorithm applies anocclusion operator that allows the selectiveattenuation and modulation of light as itpasses through plane 2. It then adds lightfrom plane 2, and propagation proceeds toplane 3. The process repeats through theother planes until the light reaches the design plane.

The ping-pong algorithm is easy to code usingstandard numerical subroutines. However, thebasic approach can only generate images of self-luminous objects, so implementing full renderingeffects and lighting models requires enhancements.It is also computationally inefficient. For these rea-sons, the ping-pong approach is not viable for apractical interactive system.

Interference-based algorithmsThese algorithms closely simulate light propa-

gation in a conventional interferometric hologramrecording. They essentially implement a 3D scalardiffraction integral capable of generating very highquality images including lighting effects and sur-face-reflection properties. Interference-based algo-rithms can reproduce all human visual depth cuesand currently serve as the benchmark for imagequality.

QinetiQ has developed this approach in itscoherent-ray-trace algorithm. This algorithm usesFourier transform geometry with an off-axis objectto incorporate advanced rendering effects such asshadowing and Phong shading of curved surfaces.The core calculation consists of a double summa-tion of all visible object points for each CGH pixel.This many-to-many mapping between CGH pix-els and object points causes high computationalloads.

Diffraction-specific algorithmsMark Lucente pioneered diffraction-specific

CGH computation while at MIT,7 with more recentvariants of the algorithm featuring Fourier-basedgeometries.5 Importantly, these algorithms allowtradeoffs in image quality with computationalspeed. Diffraction-specific algorithms consider onlythe replay of a computer-generated hologramrather than its interferometric formation as incoherent ray tracing. By controlling the final holo-

August 2005 49

Special attentionmust be paid to the

efficiency of thealgorithms used to

calculate theholographic fringe

patterns.

50 Computer

gram’s information content, which often exceedswhat the human visual system can interpret, thesealgorithms make it possible to achieve coarser res-olutions, particularly during interactive imagemanipulation, that satisfy many applications.

Diffraction-specific algorithms accomplish thisby spatially and spectrally quantizing the hologramas hogels and hogel vectors, respectively, that theycan manipulate to vary the computational load.The algorithms can precompute much of the CGHcalculation, storing the results as diffraction tablesaccessible via lookup during the interactive fringe-pattern calculation. The computational bottleneckin the latter procedure is hogel vector decoding.This step, in many forms of the algorithm, is a sim-ple multiplication and accumulation calculation(MAC), open to optimization on numerous archi-tectures.

COMPUTATIONAL LOADS To better understand CGH computational load

requirements, consider the following example. Ahorizontal-parallax-only computer-generated holo-gram with spatial resolution comparable to high-definition television and a comfortable viewingzone requires approximately 400,000 horizontaland 1,024 vertical pixels. A 4,096-pixel hogel canachieve spatial resolution exceeding commonHDTV specifications, thus generating the hologramrequires 100 hogels. Given that, on average, eachhogel must generate half of the possible imagepoints, the total estimated computational load is100 (hogels) × 1,024 (image points) × 4,096 (dif-fraction-table lookup entries) × 1,024 rows = 424giga MACs.

Spatial or temporal multiplexing can generatecolor, and requires three times as many MACs.Converting a MAC to floating-point operations,the total computational load for a single frame is2.6 teraflops, which equates to the sustained

throughput of the world’s 130th fastest computer(www.top500.org/lists/2005/06). In addition, thisfigure is for 1 frame per second; most likely a sys-tem will need to meet at least 15 fps or faster toguarantee a perceptually smooth image updateduring interactive operation. However, in certaincircumstances, optimizations can significantlyreduce the computational load.

Hardware architecturesThe computational load’s large size and method

of calculation—simple MACs from a lookuptable—largely dictate the appropriate architecture.The entire computation is naturally parallel, witheach hogel and all rows within each hogel independent. Candidate architectures includesupercomputers (massively parallel, nonuniformmemory architecture systems and high-perfor-mance clusters), special-purpose CGH computeengines such as MIT’s Cheops,8 symmetric multi-processor computers, clusters of commodity PCsand graphics processing units,9 digital signal proces-sors (DSPs), field-programmable gate arrays(FPGAs), and application-specific integrated cir-cuits (ASICs).

However, DSPs and FPGAs do not provide thekind of close, fast access to large memories thatlookup table calculations offer. In addition, ASICsare not commercially attractive given the low initialvolume of likely electroholography products. Thus,the most likely hardware architecture is based onhigh-performance computing systems.

In recent years, we have compared the perfor-mance and cost benefits of different architecturesincluding the Cray T3E, the SGI Origin, the SGIOnyx Reality Engine, and Intel IA-32 processorclusters with various interconnects. Our resultsindicate that IA-32 systems with a Myrinet inter-connect offer the most cost-effective and flexiblehigh-performance cluster.

To Active Tiling channelsOpen

GL c

omm

ands

Discrete viewpointrendering node

Core diffractionpattern and

MAC computationvia lookup tables

…

Binarizationnode

Intercept OpenGL

call stream

Intelligentlydistribute

calls

•

•

•

•

•

•

•

•

Core diffractionpattern and

MAC computationvia lookup tables

Figure 3. CGH pattern calculationpipeline for an Open GL capture,diffraction-specificalgorithm system.The display systembinarizes the hologram in the final computationalphase using a partitionedapproach and thenpasses the results tothe optical subsystem over the parallel inputarchitecture.

Parallel inputs Many SLM systems have parallel inputs, which

eases the CGH computational challenge. For exam-ple, Figure 3 shows the flow of information fromthe application or data source, with each box rep-resenting one or more processing nodes. In the sim-plest case, each row of nodes could be associatedwith a single hogel, requiring perhaps 100 rows tomeet the desired image size and field of view.Alternatively, the architecture could deploy vary-ing numbers of processing nodes at each stage toimprove the update rate or it could use one row tocompute five hogels to reduce the computationalcost.

The post-MAC result is a gray-scale CGH fringepattern with 32-bit floating-point precision. Thedisplay system binarizes the hologram in the finalcomputational phase using a partitioned approach5

and then passes the results to the optical subsystemover the parallel input architecture.

OptimizationsVarious optimizations—including pixel prioriti-

zation, dynamic level of detail, and wireframe modefor interaction5—can either accelerate CGH com-putation by increasing the frame rate or enablecomputation at the same frame rate with fewerresources, thereby reducing cost. Depending onscene geometry content, these techniques canreduce the computational load from 16 to 200times. This translates to 162 gigaflops per frame atthe most conservative level of savings. Given thata 3-GHz Intel Pentium 4 has a theoretical peakcomputational load of 12 Gflops per single-preci-sion floating point, sustained throughput could bearound 25 percent or 3 Gflops. Therefore, the com-putational load requires 54 processors per frame,or 810 for 15 fps.

QinetiQ researchers have also focused on achiev-ing high, sustained throughputs on IA-32 proces-sors using SSE2 vector capabilities to increaseefficiency. If compute performance continues toadhere to Moore’s law, the current requirement for810 IA-32 processors—or 405 dual-processor

nodes—will decrease to a modest cluster of fewerthan 100 nodes by 2006.

Finally, for electroholography to be commer-cially successful, CGH systems must exploit a rangeof standard and customized visualization softwarepackages. Ideally, users should be able to run theirexisting applications to drive the display withoutmodification. A particularly powerful approach isto capture the graphics calls that applications suchas OpenGL and DirectX produce and redirect themto the cluster-based CGH pattern calculationengine.

OPTICAL HARDWARE CONSIDERATIONS Researchers have explored a number of SLM

technologies, most notably the holovideo system10

developed by the late Steve Benton and colleaguesat MIT. Loosely based on the Scophony TV systemof the 1930s, this 18-channel, parallel acousto-optic modulator and mechanical scanner can syn-thesize a 36-million-pixel computer-generatedhologram and produce a horizontal-parallax-onlyimage of 150 × 75 × 160 mm3 (W × H × D).However, it is not possible to scale the current sys-tem to deliver the 1010 pixel counts that a practicalholographic workstation requires.

Active TilingA more recent development, and probably the

leading scalable solution, is QinetiQ’s Active Tilingsystem.11 This system exploits the existing and pro-jected strengths of both electrically addressed SLM(EASLM) and optically addressed SLM (OASLM)in an optimal way, trading the former’s high tem-poral bandwidth for the latter’s large spatial band-width.

As Figure 4a shows, an Active Tiling channeloptically replicates or “tiles” output from a fastEASLM over an area of the OASLM. One config-uration opens shutters in turn to build up the pat-tern on the write side of the OASLM, synchronizingthe shutters to the EASLM’s appropriate frames.This enables updating the entire OASLM at videorates.

August 2005 51

OpticallyaddressedSLM

Replication opticsand shutters

– Electrically addressed– SLM

(a) (b)

Figure 4. QinetiQ’sActive Tilingsystem. (a)Schematic view of a single channel,which can generate25 million pixels. (b) Photo of 1 × 4channel ActiveTiling unit capableof delivering full-color,temporallymultiplexedcomputer-generatedholograms with pixelcounts up to 10 8.

52 Computer

Active Tiling channels are modular and can bestacked together in parallel to deliver the requiredpixel counts, as Figure 4b shows. This parallelapproach also minimizes the required data feedrates for each channel and closely matches the capa-bilities of cluster-based rendering architectures.

A typical Active Tiling channel configurationconsists of a binary, 1,024 × 1,024-pixel ferroelec-tric crystal on silicon EASLM operating at a 2.5-kHz frame rate; a binary-phase diffractive opticalelement and associated refractive optics perform-ing the 5 × 5 replication; and an OASLM using anamorphous silicon photosensor, light-blocking lay-ers, a dielectric mirror, and a ferroelectric liquidcrystal output layer. The output for each channelis therefore 26 million pixels.

Current Active Tiling system attributes includea pixel areal density of more than 2.2 × 106 pixelscm-2; a compact system volume exceeding 2.4 × 109

pixels m-3; binary pixels, with a spacing of 6.6 µm;an updatable 1 × 4 channel arrangement (5,120 ×20,480 pixels = 104 megapixels) in both mono-chromatic and frame-sequential-color operation;and designs for scalable channel tilings in bothheight and width to deliver systems with a pixelcount in excess of 109.

Replay optics CGH configuration requires using various opti-

cal elements in conjunction with the SLM holdingthe holographic patterns.5 For example, many SLMpixel spacings are relatively large compared to thewavelength of replay light. In such situations, anoptical Fourier replay geometry can substantiallyreduce system volumes. Sophisticated multimirrorconfigurations, folded in three dimensions, can fur-ther minimize overall system size. Using inexpen-sive carbon fiber or electroformed mirrors can alsoreduce system costs.

User interaction An important element of CGH display systems

in many applications is user interaction with theimage. Researchers have developed intuitive inter-faces using voice, gesture, and haptics. The syner-gistic effects of 3D sound can also be exploited tofacilitate image interaction and modification.

EXPERIMENTAL RESULTS Figures 5 and 6 illustrate state-of-the-art true 3D

images generated at QinetiQ laboratories usingwhat we believe to be the largest pixel-count CGHsystems designed for this purpose. We calculatedthese computer-generated holograms using a 102-node PC Linux cluster of dual IA-32 Pentium III1.26-GHz Tualatin-core 512-Kb cache CPUs, with1 Gbyte of memory per node and two 36-GbyteUltra160 SCSI disk drives, along with a Myrinetinterconnect and a 7.5-Tbyte storage area net-work.

Figure 5 shows monochromatic and color, full-parallax, 3D images produced by a computer-generated hologram having 108 pixels, while Figure6 shows a replay of a spatial-multiplexed, 3 × 8 bil-lion-pixel, full-parallax, full-color, 3D image. Asthese images illustrate, standard computer graph-ics techniques can be incorporated into CGHdesign, including environment mapping, radiositymodeling, and transparency effects. When viewedby eye, the full images look sharp and do notappear fuzzy—the eye naturally accommodates asthe viewer focuses attention on different parts ofthe image, as with a real object.

Figure 5. Computer-generated holograms. (a) Monochromatic and (b) color, full-parallax, 3D image produced by the 10 8 pixel Active Tiling system.

Figure 6. Replay of a spatial-multiplexed, 3 × 8 billion-pixel, full-parallax, full-color, 3D image.

(a)

(b)

T he versatility of computer-generated hologra-phy, combined with its unique ability to pro-duce full-depth-cue 3D images at beyond eye

resolution, floating in space, and with an extendedcolor gamut has led some to label CGH the ultimatedisplay technology. However, many CGH-based dis-plays have an appetite for pixels that can far exceedother display types. Unique additional computa-tional operations add to the cost of such systems,particularly high-frame-rate interactive systems.Thus, for many applications, lower-cost, simpler dis-play technologies will be more appropriate.

Nevertheless, with compute power and displayhardware continuing to decrease in price and otherrequired technologies rapidly advancing, the ques-tion is not whether CGH systems will become apractical generic display technology but, rather,how soon. �

AcknowledgmentsThe authors gratefully appreciate the work of all

members of the holographic imaging project teamat QinetiQ as well as our academic and commercialcollaborators. Funding for this research comesfrom QinetiQ, the UK Ministry of Defence TG7,the Ford Motor Company, and HolographicImaging LLC.

References1. S.J.Watt et al., “Achieving Near-Correct Focus Cues

in a 3D Display Using Multiple Image Planes,” Proc.SPIE, vol. 5666, SPIE—Int’l Soc. for Optical Eng.,2005, pp. 393-401.

2. I.J. Cox and C.J.R. Sheppard, “Information Capac-ity and Resolution in an Optical System,” J. OpticalSoc. America A, vol. 3, no. 8, 1986, pp. 1152-1158.

3. J.I. Trisnadi, “Speckle Contrast Reduction in LaserProjection Displays,” Proc. SPIE, vol. 4657, SPIE—Int’l Soc. for Optical Eng., 2002, pp. 131-137.

4. C.D. Cameron et al., “Computational Challenges ofEmerging Novel True 3D Holographic Displays,”Proc. SPIE, vol. 4109, SPIE—Int’l Soc. for OpticalEng., 2001, pp. 129-140.

5. C.W. Slinger et al., “Recent Developments in Com-puter-Generated Holography: Toward a PracticalElectroholography System for Interactive 3D Visual-ization,” Proc SPIE., vol. 5290, SPIE—Int’l Soc. forOptical Eng., 2004, pp. 27-41.

6. Y. Ichioka, M. Izumi, and Y. Suzuki, “ScanningHalftone Plotter and Computer-Generated Continu-ous-Tone Hologram,” Applied Optics, vol. 10, no.2, 1971, pp. 403-411.

7. M. Lucente, “Diffraction-Specific Fringe Computa-tion for Electro-Holography,” doctoral dissertation,Dept. of Electrical Eng. and Computer Science, MIT,1994.

8. V.M. Bove Jr. and J.A. Watlington, “Cheops: AReconfigurable Data-Flow System for Video Pro-cessing,” IEEE Trans. Circuits and Systems for VideoTechnology, vol. 5, no. 2, 1995, pp. 140-149.

9. V.M. Bove Jr. et al., “Real-Time Holographic VideoImages with Commodity PC Hardware,” Proc. SPIE,vol. 5664, SPIE—Int’l Soc. for Optical Eng., 2005,pp. 255-262.

10. P. St.-Hilaire, “Scalable Optical Architecture for Elec-tronic Holography,” Optical Eng., vol. 34, no. 10,1995, pp. 2900-2911.

11. M. Stanley et al., “3D Electronic Holography Dis-play System Using a 100-Megapixel Spatial LightModulator,” Proc. SPIE, vol. 5249, SPIE—Int’l Soc.for Optical Eng., 2003, pp. 297-308.

Chris Slinger is a QinetiQ Fellow and technicaldirector of the Optronics Department at QinetiQMalvern in Great Malvern, United Kingdom. Hisresearch interests include holography, computer-generated holography, high-performance displays,and novel imaging systems. Slinger received aD.Phil. in multiple-grating volume holographyfrom the University of Oxford. He is an Instituteof Physics (IOP) Fellow. Contact him at slinger@qinetiq.com.

Colin Cameron is business development managerfor high-performance visualization in QinetiQ’sOptronics Products Group. His research interestsinclude technical computing, high-performancegraphics, and advanced display technologies forhigh-performance visualization. Cameron receiveda BSc (Hons) in laser physics and optoelectronicsfrom the University of Strathclyde, Glasgow. He isan IOP Fellow. Contact him at cdcameron@qinetiq.com.

Maurice Stanley is a QinetiQ Fellow and businessmanager for high-performance imaging in Qine-tiQ’s Optronics Products Group. His researchinterests include spatial-light-modulator technol-ogy, high-performance displays, and 3D camerasystems. Stanley received a PhD in electrical andelectronic engineering from University CollegeLondon. He is an IOP Fellow. Contact him atmstanley1@qinetiq.com.

August 2005 53