All these cameras in the market, competing for one sole purpose to produce the best quality image. Images to be so real, crisp, clear and, fine detailed. It’s a race with smartphones taking on market lead in just past five years, in a century-old industry. So far it was a race to produce the best hardware of camera and lenses to innovate and drafting the learning curve. Shapes, sizes, mobility, and compactness did their thing, sooner or later compactness will reach its physical limitation, one just cannot keep on going sticking five cameras to a smartphone and expect consumer to pay for the design cost. Computational photography on the other end of this spectrum which purely relies on the software part of digital photography.
Smartphones from 2013 are known to shoot 120 frames per second HD videos which costed around $200 but to get the same specs in a DSLR or any professional camera it would have costed more the $2000, computational photography is known to simplify the ease of taking pictures under any light conditions, from an early 2000s tech of face detection to, putting a dog face filter in 2016. Computation is the inevitable pipeline. Google Pixel 3 defines the potential of computational photography, while the whole market is trying to fit as many as six cameras in a device to grab the customers attention. Google Pixel 3 can produce stunning images if not better than optimal image quality under any major scenario with just one single lens camera.
"What if all you ever needed was a single camera application and updates for a lifetime, no more need to buy a new device every year? One camera application to administer all your image data."
Smartphones are an amazing way to connect billions of people, wireless internet enables people to stay connected to the world 24/7. The number of smartphone users is forecast to grow from 2.1 billion in 2016 to around 2.5 billion in 2019, most popular usage of smartphone is smartphone photography , which just puts the smartphone cameras in the center and on the driving seat to manure the market.
What the smartphones enabled was the access to instantly share the moments, reducing the labor time to real-time, with apps like Instagram and Snapchat people were proactive on sharing their everyday moments.
Article 30 of the General Data Protection Regulation requires enterprises to track metadata about how data is being shared and used. Such a record is important to demonstrate the state of data management but is also vital to meet the functional requirements of other Articles like data erasure, rectification, and access–without a record of where data is, it is very difficult or impossible to manage that data.
Not all cameras can produce good or even optimal quality images, though a 35mm full-frame sensor not only can physically capture more light particles some of the Pro-cameras can capture a very high dynamic range of color/tone/exposure with their RAW image computation.
Two devices; If a $100 Smartphone and a $1000 DSLR were to capture an image of the same structure, is there a way to translate the quality of the image generated by a $1000 DSLRs available RAW image metadata to help a $100 Smartphone capture better self-aware dynamic images? Algorithms like crop-factor translation and histogram matching can bring up at least 10% quality of the images produced from lower-end smartphones and we can take that quality way up by machine learning and computer vision computation to identify objects, subjects, background and foreground like elements from a better camera image data
A decentralized Camera application for smartphones to capture better quality images from any device. Backed by distributed ledger to record and store value and ownership of image data through NFT standards. Further, supplying high-quality image data to physically limiting smartphone cameras to understand and help capture better images.