April 2005 9

Grain and AliasingThe randomness of grainand therefore the randomsampling has an intrinsicbenefit in terms of preventingaliasing with patternedmaterials. The images in Fig21 and Fig 22 show theeffect grain has on thecaptured image. Figs 23 andFig 24 show how this effect isused to advantage inscanning film, especially atHD resolutions and beyond.Until electronic cameras havethe ability to use randomchanging pixel sizes andpositions, I believe aliasingwill remain an issue at theupper limits of non filmbased acquisition.

Grain and DynamicRangeThe dynamic range of filmnegative has always beenadmired, and this range isdue almost entirely to thevariance of grain size anddistribution within the filmmaterial. Without going intotoo much detail, if weassume the size of graindetermines the amount oflight captured by the grain,

then it becomes clear that asmall grain needs a lot oflight and a large grain verylittle light.

This can be seen in Fig 20.Where there is littleillumination, only the largegrains have been exposed,and where the amount oflight is greater, so a greaterrange of grain sizes isexposed. In digital terms onecould refer to the grain sizeas the bit size. The smallestgrain needs most light, so itbecomes The MostSignificant Bit. The largestgrain requires the least light,so it becomes The LeastSignificant Bit.

How does this relate to thedynamic range of film? Insimple terms if there are10,000 different grain sizesin the film emulsion, they actsomewhat like digital bits,and have the ability todifferentiate between anawful lot of light levels. Butthis still does not determinethe total contrast range. Totalrange is determined by howlarge the largest grain is

Fig.20

Fig.21

Fig.23

Below: Fig 22 - Magnified section of Fig 21

Below: Fig 24 - Magnified section of Fig 23

THE BERNARD HAPPÉ MEMORIAL LECTURE

THE FILM LOOKCan it really bedefined?The second part of Peter Swinson’s 2004 Bernard HappéMemorial Lecture explaining why material shot on film hasa unique natural ‘look’.

Bernard Happé Lecture

10 Image Technology

compared to how small thesmallest grain is. If thedifference in size is 10,000to 1 then the film can handlea brightness range of about10,000 to 1.

Grain’s great variety canreduce digital bit depthrequirements. Ever since theintroduction of Digital Filmscans, we have heard theview that we need more bitsto faithfully represent the filmimage. The figure oftenquoted is at least 14 bits percolour linear or 10 bits percolour Log. Well I wouldcontest this, so long as thegranularity is retained andthe 35mm scannedresolution is around 4K. Insuch conditions many fewerbits will suffice.And here are some imagesto prove it. (At the extreme)

Fig 25 (above) shows theoriginal 4K scanned 35mmframe with grain. Resampledat 3 bits per colour. It alsoshows the same frame grainreduced and then resampled.It is clear that the grainreduced image is notsatisfactory with 3 bits percolour; we need more bits torepresent less imageinformation!!Is it the very subtle lack ofbanding in images that

retain grain, that adds to thefilm look when displayed ondigital systems of 8 or 10 bitsper colour?"The Film Look" primesource, grain, can help in theDigital World

Grain's Primecontribution to The FilmLookStochastic Noise andStochastic Resonance. Termsnot often used in ourindustry, but paramount tounderstanding why film looksso different. Our prehistoricalvisual system has acquiredan interesting characteristic -when it comes to finerandom granularity or noise,it tries to ignore it. The brainequates such ‘noise’ to grasswaving in the breeze orleaves rustling on trees. Ithas no importance, what isimportant is the image

hiding behind or in the grassor trees. That image couldbe a leopard, tiger or anypatterned image that doesnot change over very shortperiods. Put simply. our braintries to tune noise out of ourconsciousness. It would haveto be a very big brain thatcould process so muchrandom information!

The brain's attempt to tuneout noise was demonstrated

as follows. Fig 26 (above)shows a set of verticalcolumns covered in noise.The columns were offsethorizontally and shownalternately. Everyone agreedthat the noise was fixed tothe columns, as they movedthe noise moved horizontallyas though it was part of theactual columns. Only whenthe columns were removedwas it observed that thenoise was two sets ofrandom noise, one for eachimage. Furthermore at thepoint that the columns wereremoved, most observersnoted that the two noisepatterns still moved from sideto side for a few secondsbefore it became obviousthat they really did not. Anexample of the brain tryingto filter out noise; itattributed the noise to beingpart of the pattern so that itdid not have to process itseparately. Just as it wouldhave on the grass plains ofour ancestors.

Stochastic NoiseAs it is impossible todemonstrate the effect in thisjournal, I shall attempt todescribe it.

As already noted, film'sactive capture medium israndomly sized andrandomly positioned grain,all held in the film emulsion.When an image is filmed thegrain represents the shadesof the image at eachlocation. Analyse anyindividual frame and we willsee only the shades that eachframe collected. The edgesof shades, that representresolution, may or may not,at high resolutions, bepresent depending on theposition of the grain. But filmgrain is random, thereforean edge that is notrepresented on one framemay well be in the next, andif not in the next thenprobably in the third frame.

Looking at it this way the filmimage can acquire very highresolutions if sampled overseveral frames.

Very early on I mentioned thebrain's image integratingcapabilities that cause smearon moving images. Thissame integration gives us theperception of great detail instatic or slowly moving filmimages. A series of stills from

Bernard Happé Lecture

Fig.27

Fig.26

Fig.25

April 2005 11

a film all look to have asimilar resolution, run themat 24 fps and it is amazinghow much more detailappears. Fig 27 attempts toshow the effect, whichsignificantly contributes to"The Film Look"

With electronic imageacquisition, this frame byframe randomness ismissing, since electroniccameras repeatedly sampleat the same point in everyframe.

Stochastic ResonanceThere is always the desire, atleast in the Digital world, toremove random grain ornoise. We have seen howthat may compromise bitdepth when digitizing theimage; its significance,

however is much more subtlein reference to The FilmLook.

Film Grain amplifies thebrain’s ability to see fineshading and this is known asStochastic Resonance. Fig 28(above) shows two images ofa face. The image that looksslightly "noisy", also lookssharper and with more subtledetail. But it is not. Bothimages are the same, but inthe image without "noise" wecannot see all that is there.Why not? We need to goback to look at how thevisual system works tounderstand what's going onhere. While we assume acontinuous link from the eyeall the way to the nerve cellsin the brain, the links are notactually solid as shown in Fig

29, and there is a certainthreshold at the synapticjunction between nerve cellsbelow which a signal cannotcross.

Referring to Fig 30, let'sassume that the threshold isthe pink area and the greenarea is above the threshold.The yellow wiggle representsa particular signal from theeye. What happens?For image (a) the white andblack levels are big enoughto jump across the synapse.However if the signal fromthe eye is very small becauseof only tiny changes inbrightness (b) the signalcannot cross the synapse.

But if we add to the imagesome noise or grain as in (c)the original very smallbrightness change ismodulated and is now bigenough to jump across thesynapse and because there isa real brightness change thenoise will be biased by thebrightness difference. Keepin mind that the brain filtersout the noise. Remember theswaying grass and tigersyndrome, in this sameinstance the brain will filterout a lot of the noise butretain the sense of the subtlebrightness change. Furthermore, adding noise or

grain to example (a) lets ussee more detail in theoriginal example - and in Fig31 we can now see finersubtlety.

This may explain why theimage on the right in Fig 28appears to have not onlymore sharpness but showsmore subtle detail. This is aneffect that relies entirely onfilm's subtle shading captureand grain

So, are Stochastic Resonanceand Stochastic noise themajor contributors to "TheFilm Look"? I believe theyare.

Conclusions:Contributions to "TheFilm Look"• We have seen that filmframe rates and motionsmear match the humanvisual system. In cinemas,are our eyes and brainsmore relaxed than whenclose TV viewing?

• The Slight unsteadiness offilm may take over the needfor our eyes to move around,relaxing the visual senses

• Film Grain is the Asset ofFilm, not a detractor.• Grain reduces anycoherent aliasing both at

Bernard Happé Lecture

Fig.28

Fig.29

Fig. 30

Fig. 31

12 Image Technology

source and when scanned toTV.

• Grain’s size rangedetermines the dynamiccapture range.

• Grain helps reduce digitalbit depth requirements.

• Stochastic noise andresonance provide integrateddetail frame by frame andenhance our ability to seevery subtle texture changes inthe image.

Finally I offer two conversearguments, both based onthrow back to our pre-history.

First argument: Film Is more Real thanVideoAs we have seen the humanvisual system is a verydiscerning device, honedmany years back: to spotother animals, either aspotential attackers or as afood source. Is it possiblethat our brains are stillprogrammed for this nowlong unneeded visualacutance? If this is the case,is the film's imageunsteadiness and therandom grain treated by thebrain as a more realistic viewof the world, rather than arigid vista which is morecommon to us now.

Take a still photo of a man-made structure, avoidingpeople, traffic, smoke etcand show it on a moviescreen. Tell people it is a livemovie, shot using the latestultra steady camera andnoiseless capture, who couldtell whether it was real ornot? Only if we add innatural details, such aspeople or clouds moving ortrees and grass swaying doesit immediately becomeobvious that it's a still image

Now take a similar still shoton the calmest day, of a fieldof long grass or trees, evenwith still mountains in thedistance. Show this to thesame people, I bet they willsubliminally note the lack ofthe tiny motion in the sceneand recognise it as a stillimage. AND if shown thereal scene in a movie veryfew would question thebackground mountainmoving slightly; it wouldmost likely in real lifeprehistorically have been dueto heat haze.

Is it therefore the granularityand the slight wobble thatmakes the whole scene comealive, and reminds us of ourprehistoric real vistas, withwaving grass and heat hazethat our brains are still usedto.

In other words : The FilmLook is a much more reallook, it includes minutemoving stimuli that may beimperfect but are morerealistic, or at least takesome load off having tomove our eyes. And wheresuch movement is combined,in the cinema at least, withonly seeing the image 50%of the time, this provides ourvisual senses not only with arealistic view but with a 50%reduction in brainprocessing!

Second argument:Film is not real, video isFilm is grainy and bangingabout all over the place,therefore it cannot possiblybe real. We are being told astory and there is absolutelyno chance of a tigersuddenly jumping out andeating us. Therefore webecome much more relaxed. If this is the case, how is itthat our pre-historical visual

senses do this? There wereno Cinema Caves thousandsof years ago, or indeed storytellers.

There is however one otherscenario where we normallyfeel safe that has probablynot changed over thethousands of years - Imagesin our Dreams.

Is the unreality of film actinglike a dream and causing usto relax? If so, is the ultraclean and stable videoimage reminding us of a realworld, where we must alwaysbe on guard, for who knowswhat's really out there?

So there you have it.Peter Swinson's take onThe Film Look. It'smostly in the mindwhichever way you lookat it!

© P Swinson December 2004Peter Swinson now runs anindependent consultancycompany, offering hisexperience and expertise tocompanies throughout theworld.Tel: 01727 830468email:peter_swinson@compuserve.com

DemonstrationsThe lecture was followed byan interesting four-way

demonstration of 35mm Filmand simultaneous HD videoacquisition of a "featurette"provided by Arri and shownusing both 35mm projectorand a DP 2K Cinemaprojector. Fig 32 shows thevarious combinations thatwere displayed.

Bernard Happé Lecture

Fig.32

Bernard Happé, FBKS,Vice-President of theBKSTS from 1970-72,who was responsibleduring his time asTechnical Manager atTechnicolor for manyground-breakingdevelopments inmotion pictureprocessing technology.The annual lecture inhis honour wasinaugurated in 1991.

Peter Swinson was the14th Happé MemorialLecturer.