Computational photography: The power of a few lines of code

The race of factories, supply chains and contracts has become the race of AI, of true creativity. Would you choose to invest in physical elements, or in lines of code?


When 2018 ended, the world of smartphones also reached a peak of... madness. Notch, the weirdest and ugliest screen in history, have become popular. The slider smartphone is back. All kinds of new smartphones were born…

To balance this madness, manufacturers are forced to look back at the actual needs of their users. When the chips are strong enough for mid-range smartphones to run heavy games smoothly, when the memory is big enough so that dozens of albums are no longer a challenge, when 4G is universal and 5G is on the throne, the smartphone war only remains in one battlefield: camera.

Traditional photography: physics

Referring to traditional photography is referring to physical principles: the size of the sensor, the aperture of the shutter, the focal length of the lens... A good photo will always be the result of the physical limits on sensors / lenses and the photographer's ability to take advantage of physical principles.


For example, if you want to take good photos, you need to find a camera with huge sensor size. If you want to take bokeh photos on your phone, you're almost certainly looking for smartphones with dual lenses.

But over the years, smartphones have become the most popular camera in the world. A special feature of the iPhone, Galaxy and Pixel is that they are full digital devices incorporating the most significant achievements in the field of chip development. For years, most smartphone manufacturers have forgotten this obvious fact - until Apple and Google made people pay attention to the concept of "computational photography".

Lines of code

To put it simply, computation photography does not focus on the lens or sensor, but on the later stage: processing the digital signals collected by the sensor to create JPEG images. Over the years, this has been the "least" step, with the least attention paid until Google performed a miracle: with a single camera, Pixel can still create amazing bokeh shots just like dual cameras.

The miracle here, of course, did not come from the camera cluster on the back of the device, but rather from the lines of code: by comparing between two adjacent half-pixels, Google created two different images and interpolated to distinguish model and background. Thanks to an optimal algorithm, Google could create images with more accurately blurred background than usual.



In 2018, Apple also successfully copied Google's bokeh mechanism on the single-lens of iPhone XR. Besides bokeh on a single lens, the infinite potential of the lines of code has helped Apple be able to create a feature that no other competitor has yet to catch up with: simulating studio lighting. No matter where the flash is placed on the model, Huawei or Samsung would not be able to create this feature, because what Apple has done (through the code) is to put the user in an imaginary room, and emulate the light on a person's face is based on the (imagined) position of the light.

Or even the supposedly physical feature - zooming can be digitized. Everyone knows that digital zoom will be inferior to optical zoom, but Google solves it by calculating and incorporating four digital zoom images into one sharp and accurate "large" image.

But the most significant win for computational photography in 2019 must belong to Samsung. When launched, Galaxy S10 5G of the company reached the highest point of photography, on par with Huawei P30 Pro. However, although it is better in some other areas, S10 5G lags behind P30 Pro in low-light photography: P30 Pro has a sensor twice as large and also uses RYYB, accepting giving up color accuracy in exchange for very good light capturing.


The race of factories, supply chains and contracts has become the race of AI, of true creativity. A race with no winner has turned into a race with infinite potential for users. If it were you, would you choose to invest in physical elements, or in lines of code?

By: Emma Chavez

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