COLOR

LIGHTING

  • Rendering – BRDF – Bidirectional reflectance distribution function

    http://en.wikipedia.org/wiki/Bidirectional_reflectance_distribution_function

    The bidirectional reflectance distribution function is a four-dimensional function that defines how light is reflected at an opaque surface

    http://www.cs.ucla.edu/~zhu/tutorial/An_Introduction_to_BRDF-Based_Lighting.pdf

    In general, when light interacts with matter, a complicated light-matter dynamic occurs. This interaction depends on the physical characteristics of the light as well as the physical composition and characteristics of the matter.

    That is, some of the incident light is reflected, some of the light is transmitted, and another portion of the light is absorbed by the medium itself.

    A BRDF describes how much light is reflected when light makes contact with a certain material. Similarly, a BTDF (Bi-directional Transmission Distribution Function) describes how much light is transmitted when light makes contact with a certain material

    http://www.cs.princeton.edu/~smr/cs348c-97/surveypaper.html

    It is difficult to establish exactly how far one should go in elaborating the surface model. A truly complete representation of the reflective behavior of a surface might take into account such phenomena as polarization, scattering, fluorescence, and phosphorescence, all of which might vary with position on the surface. Therefore, the variables in this complete function would be:

    incoming and outgoing angle incoming and outgoing wavelength incoming and outgoing polarization (both linear and circular) incoming and outgoing position (which might differ due to subsurface scattering) time delay between the incoming and outgoing light ray

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  • Types of Film Lights and their efficiency – CRI, Color Temperature and Luminous Efficacy

    nofilmschool.com/types-of-film-lights

     

    “Not every light performs the same way. Lights and lighting are tricky to handle. You have to plan for every circumstance. But the good news is, lighting can be adjusted. Let’s look at different factors that affect lighting in every scene you shoot. ”

    Use CRI, Luminous Efficacy and color temperature controls to match your needs.

     

    Color Temperature
    Color temperature describes the “color” of white light by a light source radiated by a perfect black body at a given temperature measured in degrees Kelvin

     

    https://www.pixelsham.com/2019/10/18/color-temperature/

     

    CRI
    “The Color Rendering Index is a measurement of how faithfully a light source reveals the colors of whatever it illuminates, it describes the ability of a light source to reveal the color of an object, as compared to the color a natural light source would provide. The highest possible CRI is 100. A CRI of 100 generally refers to a perfect black body, like a tungsten light source or the sun. ”

     

    https://www.studiobinder.com/blog/what-is-color-rendering-index/

     

     

     

    https://en.wikipedia.org/wiki/Color_rendering_index

     

    Light source CCT (K) CRI
    Low-pressure sodium (LPS/SOX) 1800 −44
    Clear mercury-vapor 6410 17
    High-pressure sodium (HPS/SON) 2100 24
    Coated mercury-vapor 3600 49
    Halophosphate warm-white fluorescent 2940 51
    Halophosphate cool-white fluorescent 4230 64
    Tri-phosphor warm-white fluorescent 2940 73
    Halophosphate cool-daylight fluorescent 6430 76
    “White” SON 2700 82
    Standard LED Lamp 2700–5000 83
    Quartz metal halide 4200 85
    Tri-phosphor cool-white fluorescent 4080 89
    High-CRI LED lamp (blue LED) 2700–5000 95
    Ceramic discharge metal-halide lamp 5400 96
    Ultra-high-CRI LED lamp (violet LED) 2700–5000 99
    Incandescent/halogen bulb 3200 100

     

    Luminous Efficacy
    Luminous efficacy is a measure of how well a light source produces visible light, watts out versus watts in, measured in lumens per watt. In other words it is a measurement that indicates the ability of a light source to emit visible light using a given amount of power. It is a ratio of the visible energy to the power that goes into the bulb.

     

    FILM LIGHT TYPES

    https://www.studiobinder.com/blog/video-lighting-kits/?utm_campaign=Weekly_Newsletter&utm_medium=email&utm_source=sendgrid&utm_term=production-lighting&utm_content=production-lighting

     

     

     

    Consumer light types

     

    https://www.researchgate.net/figure/Emission-spectra-of-different-light-sources-a-incandescent-tungsten-light-bulb-b_fig1_312320039

     

    http://dev.informationdisplay.org/IDArchive/2015/NovemberDecember/FrontlineTechnologyCandleLikeEmission.aspx

     

     

    Tungsten Lights
    Light interiors and match domestic places or office locations. Daylight.

    Advantages of Tungsten Lights
    Almost perfect color rendition
    Low cost
    Does not use mercury like CFLs (fluorescent) or mercury vapor lights
    Better color temperature than standard tungsten
    Longer life than a conventional incandescent
    Instant on to full brightness, no warm-up time, and it is dimmable

    Disadvantages of Tungsten Lights
    Extremely hot
    High power requirement
    The lamp is sensitive to oils and cannot be touched
    The bulb is capable of blowing and sending hot glass shards outward. A screen or layer of glass on the outside of the lamp can protect users.

     

     

    Hydrargyrum medium-arc iodide lights
    HMI’s are used when high output is required. They are also used to recreate sun shining through windows or to fake additional sun while shooting exteriors. HMIs can light huge areas at once.

    Advantages of HMI lights
    High light output
    Higher efficiency
    High color temperature

    Disadvantages of HMI lights:
    High cost
    High power requirement
    Dims only to about 50%
    the color temperature increases with dimming
    HMI bulbs will explode is dropped and release toxic chemicals

     

     

    Fluorescent
    Fluorescent film lighting is achieved by laying multiple tubes next to each other, combining as many as you want for the desired brightness. The good news is you can choose your bulbs to either be warm or cool depending on the scenario you’re shooting. You want to get these bulbs close to the subject because they’re not great at opening up spaces. Fluorescent lighting is used to light interiors and is more compact and cooler than tungsten or HMI lighting.

    Advantages of Fluorescent lights
    High efficiency
    Low power requirement
    Low cost
    Long lamp life
    Cool
    Capable of soft even lighting over a large area
    Lightweight

    Disadvantages of Fluorescent lights
    Flicker
    High CRI
    Domestic tubes have low CRI & poor color rendition.

     

     

    LED
    LED’s are more and more common on film sets. You can use batteries to power them. That makes them portable and sleek – no messy cabled needed. You can rig your own panels of LED lights to fit any space necessary as well. LED’s can also power Fresnel style lamp heads such as the Arri L-series.

    Advantages of LED light
    Soft, even lighting
    Pure light without UV-artifacts
    High efficiency
    Low power consumption, can be battery powered
    Excellent dimming by means of pulse width modulation control
    Long lifespan
    Environmentally friendly
    Insensitive to shock
    No risk of explosion

    Disadvantages of LED light
    High cost.
    LED’s are currently still expensive for their total light output

    (more…)

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    Read more: Types of Film Lights and their efficiency – CRI, Color Temperature and Luminous Efficacy
  • 3D Lighting Tutorial by Amaan Kram

    http://www.amaanakram.com/lightingT/part1.htm

    The goals of lighting in 3D computer graphics are more or less the same as those of real world lighting.

     

    Lighting serves a basic function of bringing out, or pushing back the shapes of objects visible from the camera’s view.
    It gives a two-dimensional image on the monitor an illusion of the third dimension-depth.

    But it does not just stop there. It gives an image its personality, its character. A scene lit in different ways can give a feeling of happiness, of sorrow, of fear etc., and it can do so in dramatic or subtle ways. Along with personality and character, lighting fills a scene with emotion that is directly transmitted to the viewer.

     

    Trying to simulate a real environment in an artificial one can be a daunting task. But even if you make your 3D rendering look absolutely photo-realistic, it doesn’t guarantee that the image carries enough emotion to elicit a “wow” from the people viewing it.

     

    Making 3D renderings photo-realistic can be hard. Putting deep emotions in them can be even harder. However, if you plan out your lighting strategy for the mood and emotion that you want your rendering to express, you make the process easier for yourself.

     

    Each light source can be broken down in to 4 distinct components and analyzed accordingly.

    · Intensity
    · Direction
    · Color
    · Size

     

    The overall thrust of this writing is to produce photo-realistic images by applying good lighting techniques.

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  • Disney’s Moana Island Scene – Free data set

    https://www.disneyanimation.com/resources/moana-island-scene/

     

    This data set contains everything necessary to render a version of the Motunui island featured in the 2016 film Moana.

     

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  • StudioBinder.com – Photography basics: What is Dynamic Range in Photography

    https://www.studiobinder.com/blog/what-is-dynamic-range-photography/

     

    https://www.hdrsoft.com/resources/dri.html#bit-depth

     

     

     

    The dynamic range is a ratio between the maximum and minimum values of a physical measurement. Its definition depends on what the dynamic range refers to.

    For a scene: Dynamic range is the ratio between the brightest and darkest parts of the scene.

     

    For a camera: Dynamic range is the ratio of saturation to noise. More specifically, the ratio of the intensity that just saturates the camera to the intensity that just lifts the camera response one standard deviation above camera noise.

     

    For a display: Dynamic range is the ratio between the maximum and minimum intensities emitted from the screen.

     

     

     

     

     

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    Read more: StudioBinder.com – Photography basics: What is Dynamic Range in Photography
  • What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…

    RASTERIZATION
    Rasterisation (or rasterization)
    is the task of taking the information described in a vector graphics format OR the vertices of triangles making 3D shapes and converting them into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes), or in other words “rasterizing” vectors or 3D models onto a 2D plane for display on a computer screen.

    For each triangle of a 3D shape, you project the corners of the triangle on the virtual screen with some math (projective geometry). Then you have the position of the 3 corners of the triangle on the pixel screen. Those 3 points have texture coordinates, so you know where in the texture are the 3 corners. The cost is proportional to the number of triangles, and is only a little bit affected by the screen resolution.

    In computer graphics, a raster graphics or bitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via a monitor, paper, or other display medium.

    With rasterization, objects on the screen are created from a mesh of virtual triangles, or polygons, that create 3D models of objects. A lot of information is associated with each vertex, including its position in space, as well as information about color, texture and its “normal,” which is used to determine the way the surface of an object is facing.

    Computers then convert the triangles of the 3D models into pixels, or dots, on a 2D screen. Each pixel can be assigned an initial color value from the data stored in the triangle vertices.

    Further pixel processing or “shading,” including changing pixel color based on how lights in the scene hit the pixel, and applying one or more textures to the pixel, combine to generate the final color applied to a pixel.

     

    The main advantage of rasterization is its speed. However, rasterization is simply the process of computing the mapping from scene geometry to pixels and does not prescribe a particular way to compute the color of those pixels. So it cannot take shading, especially the physical light, into account and it cannot promise to get a photorealistic output. That’s a big limitation of rasterization.

    There are also multiple problems:


    • If you have two triangles one is behind the other, you will draw twice all the pixels. you only keep the pixel from the triangle that is closer to you (Z-buffer), but you still do the work twice.



    • The borders of your triangles are jagged as it is hard to know if a pixel is in the triangle or out. You can do some smoothing on those, that is anti-aliasing.



    • You have to handle every triangles (including the ones behind you) and then see that they do not touch the screen at all. (we have techniques to mitigate this where we only look at triangles that are in the field of view)



    • Transparency is hard to handle (you can’t just do an average of the color of overlapping transparent triangles, you have to do it in the right order)


     

     

     

    RAY CASTING
    It is almost the exact reverse of rasterization: you start from the virtual screen instead of the vector or 3D shapes, and you project a ray, starting from each pixel of the screen, until it intersect with a triangle.

    The cost is directly correlated to the number of pixels in the screen and you need a really cheap way of finding the first triangle that intersect a ray. In the end, it is more expensive than rasterization but it will, by design, ignore the triangles that are out of the field of view.

    You can use it to continue after the first triangle it hit, to take a little bit of the color of the next one, etc… This is useful to handle the border of the triangle cleanly (less jagged) and to handle transparency correctly.

     

    RAYTRACING


    Same idea as ray casting except once you hit a triangle you reflect on it and go into a different direction. The number of reflection you allow is the “depth” of your ray tracing. The color of the pixel can be calculated, based off the light source and all the polygons it had to reflect off of to get to that screen pixel.

    The easiest way to think of ray tracing is to look around you, right now. The objects you’re seeing are illuminated by beams of light. Now turn that around and follow the path of those beams backwards from your eye to the objects that light interacts with. That’s ray tracing.

    Ray tracing is eye-oriented process that needs walking through each pixel looking for what object should be shown there, which is also can be described as a technique that follows a beam of light (in pixels) from a set point and simulates how it reacts when it encounters objects.

    Compared with rasterization, ray tracing is hard to be implemented in real time, since even one ray can be traced and processed without much trouble, but after one ray bounces off an object, it can turn into 10 rays, and those 10 can turn into 100, 1000…The increase is exponential, and the the calculation for all these rays will be time consuming.

    Historically, computer hardware hasn’t been fast enough to use these techniques in real time, such as in video games. Moviemakers can take as long as they like to render a single frame, so they do it offline in render farms. Video games have only a fraction of a second. As a result, most real-time graphics rely on the another technique called rasterization.

     

     

    PATH TRACING
    Path tracing can be used to solve more complex lighting situations.

    Path tracing is a type of ray tracing. When using path tracing for rendering, the rays only produce a single ray per bounce. The rays do not follow a defined line per bounce (to a light, for example), but rather shoot off in a random direction. The path tracing algorithm then takes a random sampling of all of the rays to create the final image. This results in sampling a variety of different types of lighting.

    When a ray hits a surface it doesn’t trace a path to every light source, instead it bounces the ray off the surface and keeps bouncing it until it hits a light source or exhausts some bounce limit.
    It then calculates the amount of light transferred all the way to the pixel, including any color information gathered from surfaces along the way.
    It then averages out the values calculated from all the paths that were traced into the scene to get the final pixel color value.

    It requires a ton of computing power and if you don’t send out enough rays per pixel or don’t trace the paths far enough into the scene then you end up with a very spotty image as many pixels fail to find any light sources from their rays. So when you increase the the samples per pixel, you can see the image quality becomes better and better.

    Ray tracing tends to be more efficient than path tracing. Basically, the render time of a ray tracer depends on the number of polygons in the scene. The more polygons you have, the longer it will take.
    Meanwhile, the rendering time of a path tracer can be indifferent to the number of polygons, but it is related to light situation: If you add a light, transparency, translucence, or other shader effects, the path tracer will slow down considerably.

     
     

     

    Sources:
    https://medium.com/@junyingw/future-of-gaming-rasterization-vs-ray-tracing-vs-path-tracing-32b334510f1f

     

     

    blogs.nvidia.com/blog/2018/03/19/whats-difference-between-ray-tracing-rasterization/

     

    https://en.wikipedia.org/wiki/Rasterisation

     

     

    https://www.quora.com/Whats-the-difference-between-ray-tracing-and-path-tracing

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  • HDRI Median Cut plugin

    www.hdrlabs.com/picturenaut/plugins.html

     

     

    Note. The Median Cut algorithm is typically used for color quantization, which involves reducing the number of colors in an image while preserving its visual quality. It doesn’t directly provide a way to identify the brightest areas in an image. However, if you’re interested in identifying the brightest areas, you might want to look into other methods like thresholding, histogram analysis, or edge detection, through openCV for example.

     

    Here is an openCV example:

     

    # bottom left coordinates = 0,0
    import numpy as np
    import cv2
    
    # Load the HDR or EXR image
    image = cv2.imread('your_image_path.exr', cv2.IMREAD_UNCHANGED)  # Load as-is without modification
    
    # Calculate the luminance from the HDR channels (assuming RGB format)
    luminance = np.dot(image[..., :3], [0.299, 0.587, 0.114])
    
    # Set a threshold value based on estimated EV
    threshold_value = 2.4  # Estimated threshold value based on 4.8 EV
    
    # Apply the threshold to identify bright areas
    # The luminance array contains the calculated luminance values for each pixel in the image. # The threshold_value is a user-defined value that represents a cutoff point, separating "bright" and "dark" areas in terms of perceived luminance.
    thresholded = (luminance > threshold_value) * 255 
    
    # Convert the thresholded image to uint8 for contour detection 
    thresholded = thresholded.astype(np.uint8) 
    
    # Find contours of the bright areas 
    contours, _ = cv2.findContours(thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 
    
    # Create a list to store the bounding boxes of bright areas 
    bright_areas = [] 
    
    # Iterate through contours and extract bounding boxes for contour in contours: 
    x, y, w, h = cv2.boundingRect(contour) 
    
    # Adjust y-coordinate based on bottom-left origin 
    y_bottom_left_origin = image.shape[0] - (y + h) bright_areas.append((x, y_bottom_left_origin, x + w, y_bottom_left_origin + h)) 
    
    # Store as (x1, y1, x2, y2) 
    # Print the identified bright areas 
    print("Bright Areas (x1, y1, x2, y2):") for area in bright_areas: print(area)

     

    More details

     

    Luminance and Exposure in an EXR Image:

    • An EXR (Extended Dynamic Range) image format is often used to store high dynamic range (HDR) images that contain a wide range of luminance values, capturing both dark and bright areas.
    • Luminance refers to the perceived brightness of a pixel in an image. In an RGB image, luminance is often calculated using a weighted sum of the red, green, and blue channels, where different weights are assigned to each channel to account for human perception.
    • In an EXR image, the pixel values can represent radiometrically accurate scene values, including actual radiance or irradiance levels. These values are directly related to the amount of light emitted or reflected by objects in the scene.

     

    The luminance line is calculating the luminance of each pixel in the image using a weighted sum of the red, green, and blue channels. The three float values [0.299, 0.587, 0.114] are the weights used to perform this calculation.

     

    These weights are based on the concept of luminosity, which aims to approximate the perceived brightness of a color by taking into account the human eye’s sensitivity to different colors. The values are often derived from the NTSC (National Television System Committee) standard, which is used in various color image processing operations.

     

    Here’s the breakdown of the float values:

    • 0.299: Weight for the red channel.
    • 0.587: Weight for the green channel.
    • 0.114: Weight for the blue channel.

     

    The weighted sum of these channels helps create a grayscale image where the pixel values represent the perceived brightness. This technique is often used when converting a color image to grayscale or when calculating luminance for certain operations, as it takes into account the human eye’s sensitivity to different colors.

     

    For the threshold, remember that the exact relationship between EV values and pixel values can depend on the tone-mapping or normalization applied to the HDR image, as well as the dynamic range of the image itself.

     

    To establish a relationship between exposure and the threshold value, you can consider the relationship between linear and logarithmic scales:

    1. Linear and Logarithmic Scales:
      • Exposure values in an EXR image are often represented in logarithmic scales, such as EV (exposure value). Each increment in EV represents a doubling or halving of the amount of light captured.
      • Threshold values for luminance thresholding are usually linear, representing an actual luminance level.
    2. Conversion Between Scales:

      • To establish a mathematical relationship, you need to convert between the logarithmic exposure scale and the linear threshold scale.

      • One common method is to use a power function. For instance, you can use a power function to convert EV to a linear intensity value.



       

      threshold_value = base_value * (2 ** EV)



      Here, EV is the exposure value, base_value is a scaling factor that determines the relationship between EV and threshold_value, and 2 ** EV is used to convert the logarithmic EV to a linear intensity value.


    3. Choosing the Base Value:
      • The base_value factor should be determined based on the dynamic range of your EXR image and the specific luminance values you are dealing with.
      • You may need to experiment with different values of base_value to achieve the desired separation of bright areas from the rest of the image.

     

    Let’s say you have an EXR image with a dynamic range of 12 EV, which is a common range for many high dynamic range images. In this case, you want to set a threshold value that corresponds to a certain number of EV above the middle gray level (which is often considered to be around 0.18).

    Here’s an example of how you might determine a base_value to achieve this:

     

    # Define the dynamic range of the image in EV
    dynamic_range = 12
    
    # Choose the desired number of EV above middle gray for thresholding
    desired_ev_above_middle_gray = 2
    
    # Calculate the threshold value based on the desired EV above middle gray
    threshold_value = 0.18 * (2 ** (desired_ev_above_middle_gray / dynamic_range))
    
    print("Threshold Value:", threshold_value)
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    Read more: HDRI Median Cut plugin