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Everything posted by tupp

  1. If you crop "into" a frame, you reduce the pixel count, therefore, you reduce to total color depth of the image. However, if you do not resize the cropped portion, its color depth per "view-degree" doesn't change from before it was cropped.
  2. Essentially, digital anamorphic systems create pseudo rectangular pixels, which are usually oblong on the horizontal axis. In such scenarios, the color depth is reduced on the horizontal dimension, while color depth remains the same on the vertical dimension. However, the total color depth of the image never changes from capture to display. Furthermore, in digital anamorphic systems the bit depth never changes on either axis (barring any intentional bit depth reduction) -- the pixels just become oblong.
  3. I am talking about actual color depth, of which viewing distance can be a factor. As I have already said, resolution is a major factor in color depth. However, in most cases, viewing distance can be ignored, as color depth can be determined by simply taking the bit depth and pixel count on a given area (percentage) of the frame. Taking "color depth per pixel" is actually just considering the number of colors that results from the bit depth in a single RGB pixel cell. Color depth also majorly involves resolution -- pixels per given area of frame (or pixel groups/cells per given area of frame). That equation merely gives the number of possible colors per RGB pixel group. It doesn't give the color depth, becuase it does not account for how many pixel groups fit into a given area of the frame. Adding an alpha channel complicates the equation considerably, in that the background color/shade can affect how many new colors that the alpha channel adds to those of the RGB cell. However, the total number of possible colors that an RGBA cell can generate will never exceed the number of possible RGB colors multiplied by the number of possible alpha shades. 8bit 4k can "practically" scale to 10bit HD with "accurate" colors. The efficiency/quality of the conversion determines how many colors are lost in translation.
  4. If you reduce the image size while maintaining the pixel count of the image, you effectively increase the resolution -- the pixels are smaller per degree of the field of view. So, you are correct in that you have increased the color depth per degree of the field of view. However, the actual total color depth of the image has not changed at all. You are merely squeezing the total color depth of the image into a smaller area, and, of course, you are sacrificing discernability and image size.
  5. The color depth of a given image can never be increased -- not without introducing something artificial. Increasing bit depth in a digital image while reducing the resolution will, at best, maintain the same color depth as the original. I think that this established theory/technique is what has been recently "discovered." Again, BIT DEPTH ≠COLOR DEPTH. Bit depth determines the number of possible shades per color channel in a digital image. Color depth is a much broader characteristic, as it majorly involves resolution and as it also applies to analog mediums (film, non-digital video, printing, etc.).
  6. @maxotics Thank you for your post. By the way, on the strength of your work with the EOS-M, I just got one with a Fujian 35mm. I can't wait to start shooting with it! In regards to my color depth post, the math that I quoted (from the page that I linked) has nothing to do with video compression. In fact, that formula only applies to raw, unadulterated image information. Introducing compression variables would make the math more complex. However, introducing compression can never increase the color depth capabilities inherent in a given image capture or image viewing system. Not sure what is the point with the Bayer images, but the color depth formula probably applies to raw Bayer images, with a slight adjustment. One chooses pixel groups in multiples of four (two green, one blue, one red), and, I think the only formula change is that one merely sums the bit depth of the two green pixels and then multiplies that sum against the bit depth of the other two pixles. Keep in mind that one is calculating the color depth of a raw image that normally (but not necessarily) has a predominant green cast. Also, be mindful of the fact that there are no Bayer viewing systems (just Bayer sensors). On the other hand, there are several non-Bayer sensors (even RGB sensors, eg. Panavision Genesis), and almost all digital color viewing systems are RGB. I do not follow the point on the formula discrepancy, but note that for the formula to work, one must choose a percentage of an image frame, and one must consistently use that same percentage for all image frames to assess their relative color depth. One can choose for the area to be the entire image, but then one is essentially taking the entire frame as one blended pixel group. If you are consistently utilizing the same image percentage throughout your example, please simplify your point for my benefit. I do not understand your conclusion, with the statement, "The number of bits that represent a color have 2 aspects." I think that I agree with the statement: "The larger the bit value the GREATER accuracy you can have in representing the color." I am not sure if "accuracy" is the appropriate term. Certainly, the larger the bit depth, the greater the number of possible colors/shades. I am not sure this statement was what you meant: "The large the bit value, the greater RANGE you can have between the same color in two neighboring pixels, say." There is a situation in which the color/shade range would be exactly the same regardless of bit depth. In addition, a greater bit depth can actually reduce the dynamic range between two pixel values. I am happy to give examples on request. Speaking of dynamic range, it really is a property that is independent from bit depth and color depth. Dynamic range involves the possible high and low value extremes relative to the noise level. The bit depth determines the number of available increments within those extremes. There are plenty of examples of systems having high dynamic range with a low bit depth (and vice versa). I agree with this statement: "Higher resolution does not create higher dynamic range." Resolution and dynamic range are completely independent. However, higher resolution definitely increases color depth. I disagree with this statement: "Dynamic range is a function of bit-depth at the pixel level." Again, bit depth and dynamic range are two different characteristics. A system can have: great bit depth and low dynamic range or low bit depth and great dynamic range -- or any other combination of the two. Thanks! [edit -- corrected formula]
  7. Most do not understand the color depth advantage of higher resolution and only see the "sharpness" advantage of higher resolution. There are common misconceptions about this scenario, so here are some basic facts. First of all, BIT DEPTH ≠COLOR DEPTH. This is the hardest concept for most to understand, but bit depth and color depth are not the same things. Basically, bit depth is a major factor in color depth, with resolution being the other major factor. A fairly accurate formula for the relationship of color depth, bit depth and resolution is: COLOR DEPTH = (BIT DEPTH X RESOLUTION)3. This mathematical relationship means that a small increase in resolution can yield a many-fold increase in color depth. The above formula is true for linear response, RGB pixel group sensors. When considering non-RGB sensors (eg. Bayer, Xtrans, etc) and sensors with a non-linear response, the formula gets more complex. In addition, the formula does not take into account factors of human perception of differing resolutions, nor does it account for binning efficiancy when trying to convert images from a higher resolution to a lower resolution. More detailed info can be found on this page.
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