0 Turn Off VR (or IS) When Shooting Sports

Computational Photography If you want sharper sports photos, and you have lenses that use VR (Vibration Reduction on Nikons) or IS (Image Stabilization on Canons), you should turn this off. There are two important reasons why: (1) the VR (or IS) slows down the speed of the autofocus, so it can stabilize the image, and (2) since you’ll be shooting at fast shutter speeds (hopefully at 1⁄ 1000 of a second or higher), you don’t get any benefit from VR (or IS), which is designed to help you in low-light, slow shutter speed situations. In fact, it works against you, because the VR (or IS) system is searching for vibration and that can cause slight...

3 Why zooming on your DSLR is different and how to use autofocus for shooting video

Computational Photography When you zoom in/out on a traditional video camera, the zoom is very smooth because it’s controlled by an internal motor—you just push a button and it smoothly zooms in or out, giving a nice professional look. The problem is that there’s no internal motor on your DSLR—you have to zoom by hand, and if you’re not really smooth with it, and really careful while you zoom, you’re going to wind up with some really choppy looking zooms. In fact, since it’s so tough to get that power-zoom quality like we’re used to with regular video cameras, there are a bunch of companies that make accessories so you can make it look like...

0 Compression Using Context Matching-Based Prediction

   The algorithm presented in this section uses both predictive and entropy coding to com- press  CFA  data.     First,  the  CFA  image  is  separated  into  the  luminance  subimage  (Fig- ure 3.1b) containing all green samples and the chrominance subimage (Figure 3.1c) con- taining all red and blue samples. These two subimages are encoded sequentially. Samples in the same subimage are raster-scanned and each one of them undergoes a prediction pro- cess based on context matching and an entropy coding process as shown in Figure 3.3a. Due to the higher number...

0 What to do if your image isn’t quite good enough to print?

Image printingComputational photography If you’ve taken a shot that you really, really love, and it’s maybe not as sharp as you’d like it to be, or maybe you’ve cropped it and you don’t have enough resolution to print it at the size you’d like, I’ve got a solution for you—print it to canvas. You can absolutely get away with murder when you have your prints done on canvas. With its thick texture and intentionally soft look, it covers a multitude of sins, and images that would look pretty bad as a print on paper, look absolutely wonderful on canvas. It’s an incredibly forgiving medium, and most places will print custom sizes of whatever...

0 Common Compression Techniques

Inside sony  In lossless compression, bits are reduced by removing the redundant information carried by an image. Various techniques can be used to extract and remove the redundant informa- tion by exploring i) the spatial correlation among image pixels, ii) the correlation among different color channels of the image, and iii) the statistical characteristic of selected data entities extracted from the image. The performance of an algorithm depends on how much redundant information can be removed effectively.       A compression algorithm usually ex- ploits more than one technique to achieve the goal.  Entropy coding...

0 Color Fidelity versus Spatial Resolution

computational photography  The family of CFA patterns selected in this chapter is motivated by recalling contrast sensitivity research showing human sensitivity to luminance contrast is very different from human sensitivity to chrominance contrast.      Reference [70] examines the dependence of chrominance contrast sensitivity on spatial frequency and on illuminance level; it was found that contrast sensitivity degrades at lower luminance levels, for both chrominance and lumi- nance. Despite limited comparison with luminance contrast sensitivity, the results suggest that chrominance sensitivity degrades past one cycle/degree,...

0 Why JPEG look better than RAW images?

JPEG or RAW Computational photography I know what you’re thinking, “I’ve always heard it’s better to shoot in RAW!” It may be (more on that in a moment), but I thought you should know why, right out of the camera, JPEG images look better than RAW images.  It’s because when you shoot in JPEG mode, your camera applies sharpening, contrast, color saturation, and all sorts of little tweaks to create a fully processed, good-looking final image. However, when you switch your camera to shoot in RAW mode, you’re telling the camera, “Turn off the sharpening, turn off the contrast, turn off the color saturation, and turn off all those tweaks...

0 Computational photography table 2

Computational Photography  Summary of Relative Noise in White Balanced and Color Corrected Signals.          QE Set        SB        TrSB    L B              SC           Tr SC   L C                   397 000 000                    1038   250     082          RGB      000 262 000     9.88   1.24    250     625  ...

0 Computational photography table

Summary  of  channel  sensitivity  and  color  correction  matrices. The  balance  gains  and  the        sensitivity gain are respectively denoted by G  G  G   and GE .                                              1  2  3         QE Set   Channel Response     G  G  G          GE             M          ...