0 Save 30% on Jared Polin’s Top Quality Photography Training

People often mention to me that they feel as though they’re not making the most from their dSLR cameras. They know their camera has potential for taking amazing shots but are not quite sure how get control over it.

Our Photo Nuts series of eBooks are in part designed to help change that, however I realise that eBooks are not a format everyone enjoys.  This week I came across a product that just might help you unlock the power of your dSLR – particularly if videos are more your thing.  

Many regular dPS readers will know Jared Polin (AKA FroKnowsPhoto… the guy who always wears the ‘I Shoot RAW’ tshirts) because we’ve featured his videos here on the site before. We featuring him because the comments we get from readers are that they not only learn a lot from him but that he teaches in a fun and relatable way.

Jared has just released a 3-hour video guide designed specifically to help you get out of auto mode. I’ve spent some time going through it in the last few days and it is really good.

Best of all – as a launch special Jared is currently offering a 30% discount on it.

Right now you can own this course for just $67 (regular price is $97).  The course is instantly downloadable once you order and it’s really good (both content but also the quality of the video).

Check it out here.

Jared’s put together a short video (below) to let you know more about the course. Or you can skip the video and head over to FroKnowsPhoto and grab yourself a copy.

View the original article here

0 Portrait of Two People: Weekly Photography Challenge

This week your photographic challenge is to take and share a portrait of two people.

This is a followup to our recent post – how to take creative couples portraits – although you’re more than welcome to share two people that are not a ‘couple’ as such if you wish.

It could be a couple but it could also be two siblings, a parent and child, two workmates, two strangers… whatever you like.

You’re welcome to take a posed portrait or something less formal or even a candid one. Really it’s up to you!

Once you’ve taken and selected the ‘ZOOM’ image that you’d like to share – upload it to your favourite photo sharing site or blog and either share a link to it or – embed them in the comments using our embed tool to do so.

If you tag your photos on Flickr, Instagram, Twitter or other sites with Tagging tag them as #DPSCOUPLE to help others find them. Linking back to this page might also help others know what you’re doing so that they can share in the fun.

Also – don’t forget to check out some of the great shots posted in last weeks challenge – Zoom challenge where there were some great shots submitted.

View the original article here

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 of green samples in the CFA image compared to red or blue
samples, the luminance subimage is encoded before encoding the chrominance subimage.
When handling the chrominance subimage, the luminance subimage is used as a reference
to remove the interchannel correlation.

Figure 3.3

 Decoding is just the reverse process of encoding as shown in Figure 3.3b. The lumi-
nance subimage is decoded first to be used as a reference when decoding the chrominance
subimage. The original CFA image is reconstructed by combining the two subimages.


Context Matching-Based Prediction 

   In the prediction process exploited here, the value of a pixel is predicted with its four
closest processed neighbors in the same sub-image. The four closest neighbors from the
same color channel as the pixel of interest should have the highest correlation to the pixel
to be predicted in different directions and hence the best prediction result can be expected.
These four neighbors are ranked according to how close their contexts are to the context
of the pixel to be predicted and their values are weighted according to their ranking order.
Pixels with closer contexts to that of the pixel of interest contribute more to its predicted
value. The details of its realization in handling the two subimages are given below.

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

Image printing
Computational 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 you want, so if
you’ve had to crop the photo to a weird size, that usually doesn’t freak them out. Give
it a try the next time you have one of those photos that you’re worried about, from a
sharpness, size, or resolution viewpoint, and I bet you’ll be amazed!

Raw or Jpeg images?

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 you do to
make the image look really good, and instead just give me the raw, untouched photo
and I’ll add all those things myself in Photoshop or Lightroom” (or whatever software you
choose). So, while RAW files have more data, which is better, the look of the RAW file
is not better (it’s not as sharp, or vibrant, or contrasty), so it’s up to you to add all those
things in post-processing.

Now, if you’re pretty good in Photoshop, Lightroom, etc., the
good news is you can probably do a better job tweaking your photo than your camera
does when it creates a JPEG, so the final result is photos processed just the way you like
them (with the amount of sharpening you want added, the amount of color vibrance you
want, etc.). If you just read this and thought, “Man, I don’t even use Photoshop…” or
“I don’t really understand Photoshop,” then you’ll probably get better-looking images by
shooting in JPEG and letting the camera do the work. I know this goes against every-
thing you’ve read in online forums full of strangers who sound very convincing, but I’ll
also bet nobody told you that shooting in RAW strips away all the sharpening, vibrance,
and contrast either. Hey, at least now you know.

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    390    24.41   1.60
                  000 000 329                     082   390     778

                  397 000 000                    1822  1470     724
         RPB      000 100 000     8.26   0.84   1470    2168  1394     53.22   2.51
                  000 000 329                     724  1394    1332

                  202 000 000                    2505   643    471
         CMY      000 189 000     5.67   1.03    643    1045    828    51.34   1.90
                  000 000 175                    471    828    1584

  In the case where  2   P   G       G , where i  123, this simplifies to
                              C    E   i
                      g

                                                 
                                         G1  0   0
                                                 
                                K         0 G2   0   G  P                              (1.8)
                                  B                    E C
                                          0  0  G3

To focus on the relative sensitivity, the matrix S is defined by leaving out the factor of P  :
                                                B                                         C

           
                                          G1  0   0
                                                   
                                  SB      0  G2   0   GE                               (1.9)
                                          0   0  G3

The values on the diagonal of SB   show the relative noise levels in white balanced images
before color correction, accounting for the differences in photometric sensitivity. To finish
the comparison, the matrix S   is defined as MS  MT . The values on the diagonal of S     in-
                             C                  B                                      C
dicate the relative noise levels in color corrected images. The values L B  and L C indicate
the estimated relative standard deviation for a luminance channel based on Equation 1.4.

 As shown in Table 1.2, the TrSB  and L B      are smaller for CMY and for RPB than for
RGB, reflecting the sensitivity advantage of the broader spectral sensitivities.   However,
TrSC  and L C  are greater for RPB and CMY than for RGB, reflecting the noise ampli-
fication from the color correction matrix.  In summary, while optimal selection of spectral
sensitivity  is  important  for  limiting  noise,  a  well-selected  relatively  narrow  set  of  RGB
spectral sensitivies is close to optimum, as found in References [65] and [66]. Given these
results, it is tempting to consider narrower spectral bands for each color channel, reduc-
ing the need for color correction.  This would help to a limited extent, but eventually the
signal loss from narrower bands would take over.     Further, narrower spectral sensitivities
would produce substantially larger color errors, leading to lower overall image quality. The
fundamental problem is that providing acceptable color reproduction constrains the three
channel system, precluding substantial improvement in sensitivity.

Reference  [65] considers the possibility of reducing the color saturation of the image,
lowering the noise level at the expense of larger color errors.   However,  the concept of
lowering the color saturation can be applied with RGB quantum efficiencies as well.  Ref-
erence [66] shows that by allowing larger color errors at higher exposure index values, the

optimum set of quantum efficiencies changes with exposure index.  In particular, at a high 

exposure index, the optimum red quantum efficiency peaks at a longer wavelength and has 

less overlap with the green channel.    This is another way to accept larger color errors to 

reduce the noise in the color corrected image. 

Don't miss to read computational photography table 1!

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
                                         1  2  3

                                                               1558  0531  0027
        RGB      2616 3972 3159     1518 1000 1257    2.616  0078     1477  0399
                                                               0039  0508    1469

                                                               2000  1373    0373
        RPB      2616  10390 3159   3972 1000 3289    1.000  1062     3384 1322
                                                               0412  1248    1836

                                                             2554     2021   1533
        CMY      5134 5486 5929     1155 1081 1000    1.752    0941  1512    1571
                                                               1201   1783  1984

reflectance,  Qi is the quantum efficiency,  and IEI is the exposure index.  The additional
values are Planck’s constant h, the speed of light c, the spectral luminous efficiency function
V ,  and  normalization  constants  arising  from  the  definition  of  exposure  index. Using  a
relative spectral power distribution of D65 for the illuminant, a pixel size of l  22  m,
and a spectrally flat 100% diffuse reflector, the mean number of photo-electrons captured
in each pixel at an exposure index of ISO 1000 are shown under “Channel Response” in
Table 1.1.


The balance gains listed are factors to equalize the color channel responses.  The sensi-
tivity gain shown is calculated to equalize the white balanced pixel values for all sets of
quantum efficiencies. The color correction matrix shown for each set of quantum efficien-
cies was computed by calculating Equation 1.5 for 64 different color patch spectra, then
finding a color correction matrix that minimized errors between color corrected camera
data and scene colorimetry, as described in Reference [68].
 
The illustration compares the noise level in images captured at the same exposure index
and corrected to pixel value P  . For a neutral, the mean of the balanced pixel values is
                               C
the same as the color corrected pixel values.  Since the raw signals are related to the bal-
anced signal by the gains shown in Table 1.1, the original signal levels can be expressed as
follows:
                                      1    0     0        
                                                        P
                                    GE G1                 C
                                                     
                                            1
                                                           
                            P         0          0      P                            (1.6)
                                                     
                             O            GE G2           C
                                      0    0     1      P
                                                          C
                                                GE G3


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