That little rectangular [yes almost every time] hole which sits in the middle of digital camera in-side the round embossed figure and has shining elements is called as sensor (for now I guess its ok to read this way).

This element called as sensor does the job of converting optical image to digital version after capturing light and converting into an electrical signals thus given a name as sensor. Make sure you never touch it. [ ya its like “touch me not” kind of thing].

The number of pixels, size of pixels, pixel per mili meter square (sensor dimensions are normally in mm^2), quality, architecture, size and make up of the image sensor, combined together with the lens and image processor, play a massive part in the image quality produced by the camera.

Camera Sensors

By now we know camera sensors are the “light-sensitive components” that capture light and convert it into an electrical signal. There are two main types architecture of camera sensors.

  • CCD (charge-coupled device) – Known as “Global shutter” as it exposes all the pixels at the same time
    • Electron-multiplying charge-coupled device (EMCCD)
  • CMOS (complementary metal-oxide-semiconductor). – Known as “Rolling shutter” as it expose one pixel row at a time.
    • Back-illuminated CMOS – Have reputation as better the CMOS

The size of the sensor affects the field of view and depth of field of a camera, with larger sensors generally producing better image quality.

The resolution of the sensor is measured in megapixels, which indicate the number of pixels that make up the sensor. More megapixels do not necessarily mean better image quality, other factors such as the size of the sensor, image processor, and lens quality also play important roles.

Resolution – More Pixels or Bigger Pixels

Higher resolution does not always produce sharp images so in short higher resolution does not translate directly to sharpness of the image. How much zoom into the image without loosing the details is very subjective.

Having same sensor size, architecture, type and quality as first set of tools then we can look at the question more pixels or bigger pixels, in my opinion and personal exp on full frame and APS-C camera’s, its the matter of what output you want like

  • Street, landscape or architecture – Go for bigger pixels (off-cource there are some consideration and correct balance)
  • Portrait, Sports and animal kingdom – go for more pixels , again correct balance (pixels per mm^2), depth of field and lightings cant be ignored


What resolution do I need for my images?

Before answering that question there are many questions needs to be answered before demanding the quality for the image i.e some of the questions like below

  • What are you photographing?
  • How is it being displayed?
  • Who is viewing it ?
  • From what distance the image will be viewed?
  • If you plan to print then mind the dpi (dots per inch) otherwise mindful of ppi(pixels per inch)

You can not forget lens and its performances especially on the edges (assuming center performance of almost all lenses are good enough if not excellent). These are the sorts of things you need to first ask yourself if you’re to answer the above question.

Some calculations are as below

Points to Note:

All credits if any remains on the original contributor only. We have covered the Importance of Camera Sensors (Type, Size etc) for basic explanations. Mega Pixels (Camera Resolution) and factors around them and relationship with sensors at high-level of understanding. In the next upcoming post will talk about AI in photography.

Feedback & Further Question

Do you have any questions about Photography, Machine Learning or Big Data? Leave a comment or ask your question via email. Will try my best to answer it.

Books + Other readings Referred

  • Open Internet – Research Papers and ebooks
  • Personal hand on work on data & experience of  @AILabPage members
  • Book – Basics of Imaging

Posted by V Sharma

Technology specialist in Financial Technology(FinTech), Photography, Artificial Intelligence. Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Mobile Payments), Data Science, IT Service Management, Machine Learning, Neural Networks and Deep Learning techniques. Mobile Data and Billing & Prepaid Charging Services (IN, OCS & CVBS) with over 15 years experience. Led start ups & new business units successfully at local and international levels with Hands-on Engineering & Business Strategy.

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