1 / 31

Filtering and Color

Filtering and Color. To filter a color image, simply filter each of R,G and B separately Re-scaling and truncating are more difficult to implement: Adjusting each channel separately may change color significantly

vcarpenter
Download Presentation

Filtering and Color

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Filtering and Color • To filter a color image, simply filter each of R,G and B separately • Re-scaling and truncating are more difficult to implement: • Adjusting each channel separately may change color significantly • Adjusting intensity while keeping hue and saturation may be best, although some loss of saturation is probably OK

  2. Compositing • Compositing combines components from two or more images to make a new image • The basis for film special effects (even before computers) • Create digital imagery and composite it into live action • Important part of animation – even hand animation • Background change more slowly than foregrounds, so composite foreground elements onto constant background

  3. Very Simple Example = over

  4. Mattes • A matte is an image that shows which parts of another image are foreground objects • Term dates from film editing and cartoon production • To composite with a matte: • Take foreground pixels over white parts of the matte and copy them into the background image

  5. Alpha • Basic idea: Encode opacity information in the image • Add an extra channel, the alpha channel, to each image • alpha = 1 implies full opacity at a pixel • alpha = 0 implies completely clear pixels • Images are now in RGBA format, and typically 32 bits per pixel (8 bits for alpha)

  6. Smoothing Edges • Reduce alpha gradually at edges to smooth them

  7. Pre-Multiplied Alpha • Instead of storing (R,G,B,), store (R,G,B,) • The compositing operations in the next several slides are easier with pre-multiplied alpha • To display and do color conversions, must extract RGB by dividing out  • =0 is always black • Some loss of precision as  gets small, but generally not a problem

  8. Alpha and Translucent Objects • If the image is of a translucent object, then  represents the amount of the background that is blocked • When combining two translucent objects: • (1-a)(1-b) of the background shows through both • a(1-b) passes through B but is blocked by A • b(1-a) passes through A but is blocked by B • ab of the background is blocked by both

  9. Alpha and Opaque Objects • Assume a pixel represents the color of a small area • Typically a square, but not necessarily • Interpret  to represent the fraction of the pixel area covered by an object • Question: When we combine two images, how much of the pixel is covered? • What should the new  be?

  10. Sub-Pixel Configurations • We will assume partial overlap, implying that we have no specific knowledge of the sub-pixel structure No overlap o= a+ b Full overlap o= b Partial overlap o= a+ (1-a)b

  11. Compositing Assumptions • We will combine two images, f and g, to get a third composite image • Not necessary that one be foreground and background • Background can remain unspecified • Both images are the same size and use the same color representation • Multiple images can be combined in stages, operating on two at a time

  12. Sample Images

  13. Image Decomposition • The composite image can be broken into regions • Parts covered by f only • Parts covered by g only • Parts covered by f and g • Parts covered by neither f nor g • Same goes for sub-pixels in places where 1

  14. Sample Decomposition

  15. Basic Compositing Operation • The different compositing operations define which image “wins” in each sub-region of the composite • At each pixel, combine the pixel data from f and the pixel data from g with the equation: • F and G describe how much of each input image survives, and cf and cg are pre-multiplied pixels, and all four channels are calculated

  16. “Over” Operator • Computes composite with the rule that f covers g

  17. “Over” Operator

  18. “Inside” Operator • Computes composite with the rule that only parts of f that are inside g contribute

  19. “Inside” Operator

  20. “Outside” Operator • Computes composite with the rule that only parts of f that are outside g contribute

  21. “Outside” Operator

  22. “Atop” Operator • Computes composite with the over rule but restricted to places where there is some g

  23. “Atop” Operator

  24. “Xor” Operator • Computes composite with the rule that f contributes where there is no g, and g contributes where there is no f

  25. “Xor” Operator

  26. “Clear” Operator • Computes a clear composite • Note that (0,0,0,>0) is a partially opaque black pixel, whereas (0,0,0,0) is fully transparent, and hence has no color

  27. “Set” Operator • Computes composite by setting it to equal f • Copies f into the composite

  28. Unary Operators • Darken: Makes an image darker (or lighter) without affecting its opacity • Dissolve: Makes an image transparent without affecting its color

  29. “PLUS” Operator • Computes composite by simply adding f and g, with no overlap rules • Useful for defining cross dissolve in terms of compositing:

  30. Obtaining  Values • Hand generate (paint a grayscale image) • Automatically create by segmenting an image into foreground background: • Blue-screening is the analog method • Remarkably complex to get right • “Lasso” is the Photoshop operation • With synthetic imagery, use a special background color that does not occur in the foreground • Brightest blue is common

  31. Compositing With Depth • Can store pixel “depth” instead of alpha • Then, compositing can truly take into account foreground and background • Generally only possible with synthetic imagery • Image Based Rendering is an area of graphics that, in part, tries to composite photographs taking into account depth

More Related