All Things Color for Film and Digital Cinema
Subscribe | Log in


DI Movie Posters

Swinging Safari







Baulkham hills




a href=””>222-Thai-Poster








Only The Dead


a82312c02wuwish you were here



wasted-on-the-young-movie-poster-2010-10206931084407CANE TOADS 1SHT.inddjohn_doe_vigilanteneedleimagesa_heartbeat_away_poster015319da9f9dd9313subdivisionstorage-poster_280x415600full-lake-mungo-posterirresistible_bThe_Jammed_Film






A nice little freeware Mac OS calibration app for Computer/Laptop Screens


“SuperCal™ is a visual display calibrator capable of measuring and correcting most conventional displays, including LCDs, CRTs and projectors. SuperCal doesn’t require any hardware measurement devices – only your eyeballs – yet it can be much more accurate, based on how well you pay attention to what you’re doing.”

SuperCal™ by bergdesign is a simple to use screen calibration tool. If youve struggled in the past with the Mac OS tools to calibrate your computer/ laptop screen then this will definitely help without the expense of a full calibration Kit.

Results of this software, when calibrating by eye, are far superior to the stock standard tools found in MacOS screen prefs.

Blacks look a lot better, Gamma is better and white point is good as well. If you want to get into saturation and color gamut you’d have to get the full version.

The software can be downloaded from here:


Color Blindness – An amazing experiment –


Today I performed an interesting yet simple experiment.


Marcie - Color Test

Marcie - Color Test - Fig A


Regarding the LAST POST ON COLOR BLINDNESS I began pondering what an emulation of Color Blindness would look to to a person with such a “visually challenged color perception”.

As a test I invited two colleges to view an image both as natural color original and as a emulation of Color Blindness.

Here is the website I used to view the color emulation.

Both images looked near identical to each viewer.

BUT here is the amazing bit. 

Using a Macbeth color chart Fig.B as a test, I then asked my friends to label the colors they saw.

Once again to their eyes the two images were ‘the same’ in color…….


Macbeth Test

Macbeth Test - Fig .B

No Macbeth  Name           Red/Green Deficient Name A      Red/Green Deficient Name B      
1 dark skin Dark Chocolate     Brown      
2 light skin Skin     Tan      
3 blue sky Light Blue     Light Blue      
4 foliage Olive Green     Green      
5 blue flower Light Blue     Light Blue      
6 bluish green Washed out green     Tan      
7 orange Terra-cotta     Light Green      
8 purplish blue Ultramarine     Purple      
9 moderate red Red Brick     green      
10 purple Night Sky      Purple      
11 yellow green Ochre Yellow     Yellow      
12 orange yellow Sunday Latte     Orange OR Green      
13 blue Meadow Morning     Blue      
14 green UltraMarine     Green      
15 red Brick Red     Green      
16 yellow Yellow Ochre     Yellow      
17 magenta Fuchsia – Lilac     Pink      
18 cyan Light Blue     Light Blue      
19 white Polar Scape            
20 neutral 8 Polar Scape 2            

Friend A interestingly noted wearing Polarized lenses made colors more intense and therefor more distinguishable.

Doubly interesting was the fact that Friend A  really liked 3D movies as the colors became more intense. I wonder if this is because the eye is being given two distinct references of polarized spectral information to help distinguish the colors.

I found it amazing that both friends saw the Pink in the aparently Grey patch on line 3 of the simulation Macbeth Chart. It seems almost identical to the grey patch below it. Anecdotally, Friend B told me a story about ordering a new grey kitchen bench a while back. He was mortified on coming home one day to find his new pink kitchen bench!


Adrian Hauser

18 Percent Grey …. “Middle Grey” and Magic Numbers


    For a while I have been pondering the function of the 18 percent grey card. Why 18%, what is its historical reference…ect.

    After a lot of reference reading I came up with the following ….

    Traditionally 18 % refers to the statistical average reflectance of a photographed scene. ‘Normally’ exposed skin tones also generate an average incident light reading of about 18%.

    Photographically, if you were to make a set of 11 patches starting with 100% reflectivity and each subsequent patch was halved in reflectance, you would end up with a logarithmic scale where the light intensity is being halved each patch. The seventh step, middle grey, would yield a photographic Status M Neg density value of 0.7

    Using our Density math described in previous posts we know Transmission = 1/10^Density   

    Therefor    T=1/10^0.7= 0.18      AND       Transmission is directly proportionate to Luminance L*. Not boring you with the math the result is 49.496.

   So    0.7 Density = 0.18 Transmission = 50% Luminance(L*).



Fig.A - Status M against Status A Density

Fig.A - Status M against Status A Density



  Interestingly If we map our 21 step sensitometry readings of a 21 strip grey scale test wedge over Neg and Print densities we see that 0.7D is the cross over point !   See Fig.A









Looking at the image below, Fig.C, you will notice that this point of around 18 % grey is the mid point at which the cineon Log file is expanded when overlayed with a Print emulation 3DLookUp table. Both Mid Grey LAD Patch’s are almost identical as seen on the corresponding waveform Representations. 






Looking at the graph in Fig.C one can also see the reference point of 18 % on each of the mapped targets averaging around 50% luminance. Interestingly with this chart I have mapped Cineon Log levels against the 2.2 and 2.5 video gamma transfer functions and CIE Luminace L* values. They are all relatively close to each other in their Logarithmic encoding. I can see im going to have to rewrite this as its going to get messy from about here on in, although quite fascinating. 2.5 gamma looks like its the best match for CIE L* but somehow we got stuck with 2.2. Near enough is good enough I guess. It was decided back in the 8Bit video days that to help save on visual data that video/TV could also have a perceptual gamma encoding, once again mimicking the eyes response to nature. A gamma of 2.2 was decided apon.  




The Macbeth Chart

The Macbeth chart is a color evaluation chart that represents and reflects the colors of nature under any illumination. It is used as a color rendition and reproduction target for both stills and motion picture imaging labs. Using the  given set of digital scientific values for each patch an operator can evaluate a given image exposed with the card and correct for color inconsistencies inherent in Digital imaging devices, film stocks, lens coatings, old lamps, printing stocks, printing paper, monitoring devices,….ect.

Below is the Macbeth Color Chart and its relative CIE and SMPTE-C RGB values.

Macbeth Color Chart

Macbeth Color Chart










No Name CIE_x CIE_y CIE_Y Hue Value Chroma RGB_TRIPLET
1 dark skin 0.400 0.350 10.1 3 YR 3.7 3.2 116 80 67
2 light skin 0.377 0.345 35.8 2.2 YR 6.47 4.1 196 149 129
3 blue sky 0.247 0.251 19.3 4.3 PB 4.95 5.5 91 122 155
4 foliage 0.337 0.422 13.3 6.7 GY 4.2 4.1 88 109 67
5 blue flower 0.265 0.240 24.3 9.7 PB 5.47 6.7 128 127 175
6 bluish green 0.261 0.343 43.1 2.5 BG 7 6 90 190 169
7 orange 0.506 0.407 30.1 5 YR 6 11 221 119 44
8 purplish blue 0.211 0.175 12.0 7.5 PB 4 10.7 71 90 164
9 moderate red 0.453 0.306 19.8 2.5 R 5 10 198 81 98
10 purple 0.285 0.202 6.6 5 P 3 7 93 60 107
11 yellow green 0.380 0.489 44.3 5 GY 7.1 9.1 158 88 64
12 orange yellow 0.473 0.438 43.1 10 YR 7 10.5 234 161 49
13 blue 0.187 0.129 6.1 7.5 PB 2.9 12.7 47 59 151
14 green 0.305 0.478 23.4 0.25 G 5.4 8.65 65 150 71
15 red 0.539 0.313 12.0 5 R 4 12 181 40 59
16 yellow 0.448 0.470 59.1 5 Y 8 11.1 241 200 38
17 magenta 0.364 0.233 19.8 2.5 RP 5 12 190 78 146
18 cyan 0.196 0.252 19.8 5 B 5 8 0 134 164
19 white 0.310 0.316 90.0 N 9.5 0 242 242 236
20 neutral 8 0.310 0.316 59.1 N 8 0 200 200 199
21 neutral 6.5 0.310 0.316 36.2 N 6.5 0 159 160 159
22 neutral 5 0.310 0.316 19.8 N 5 0 122 121 119
23 neutral 3.5 0.310 0.316 9.0 N 3.5 0 84 84 84
24 black 0.310 0.316 3.1 N 2 0 53 53 53 

Color Blindness….. and color fatigue


An image from the ishihara test for color blindness

An image from the ishihara test for color blindness

Color Blindness and color fatigue has been the cause of quite a few conundrums in the grading suite.

We have all debated with friends, at some point in our lives, the perception of color between each other. “Do you see the blue of the sky the same way I do?” Someone who has been color blind all their lives takes their perception of color as gospel. 

What’s interesting is when grading a project over a few hours without a break or watching a feature film your internal reference point of what is a correct Balanced Image easily shifts. Technically it only takes 40 minutes for ones Color reference to become ‘reset’, taking on what is presented to you as the new correct reference. In a dark theatre without any external color reference point this is very easy. In this way I find TV grading and feature film grading two quite different beasts. In a darkened theatre our perception of subtle  changes in dark tones is much more than that of a typical television viewing environment where traditionally images are ‘pumped’ to jump off the screen.  

With this in mind can one be trained to be temporarily color blind in 40 mins?

I am currently making up some look up tables that emulate different color vision deficiencies. I’d love to use them some day in a film where perhaps one of the characters is color blind.

An interesting site is this one where you can type in a web address and it will show you how that page or image is percieved by someone deficient in say, Red/Green color.

Here is the site to go to to check your color vision.

Ishihara Test





Lenna - Common library Image used for Graphics apps testing.

Lenna - Common library Image used for Graphics apps testing.

This Category will be a reference for color theories and concepts not necessarily related to film.

See All Posts Here.

Color Reference


This is Lenna Sjooblom. She was a 1972 Playboy Playmate whose centerefold image became one of the famous images included in a graphics image processing image  library. The Image library is used to test Image processing algorithms. Lena, having graced the desktops of Millions of Developers over the years, attended a developer conference 50 years later for the Society of Imaging Science and Technology.